Machine

Learning

in

Low-level

Microarray

Analysis

Machine

learning

and

data

mining

have

found

a

multitude

of

successful

applications

in

microarray

analysis

with

gene

clustering

and

classification

of

tissue

samples

being

widely

cited

examples

Low-level

microarray

analysis

often

associated

with

the

pre-processing

stage

within

the

microarray

life-cycle

has

increasingly

become

an

area

of

active

research

traditionally

involving

techniques

from

classical

statistics

This

paper

explores

opportunities

for

the

application

of

machine

learning

and

data

mining

methods

to

several

important

low-level

microarray

analysis

problems

monitoring

gene

expression

transcript

discovery

genotyping

and

resequencing

Relevant

methods

and

ideas

from

the

machine

learning

community

include

semi-supervised

learning

learning

from

heterogeneous

data

and

incremental

learning

DNA

microarrays

have

revolutionized

biological

research

over

the

short

time

since

their

inception

-LSB-

-RSB-

Although

most

widely

used

for

parallel

measurement

of

gene

expression

-LSB-

-RSB-

microarrays

are

starting

to

find

common

application

in

other

areas

of

genomics

and

transcriptomics

including

genomic

re-sequencing

-LSB-

-RSB-

genotyping

-LSB-

-RSB-

and

transcript

discovery

-LSB-

-RSB-

Research

labs

armed

with

microarrays

have

been

able

to

partake

in

a

range

of

studies

including

finding

gene

function

-LSB-

-RSB-

correcting

mistaken

database

annotations

-LSB-

-RSB-

performing

linkage

analyses

determining

specific

genes

involved

in

biological

pathways

identifying

genes

that

are

important

at

certain

times

of

development

-LRB-

or

that

are

turned

on/off

over

a

course

of

treatment

-RRB-

elucidating

gene

regulatory

networks

-LSB-

-RSB-

diagnosing

disease

in

tissue

sam-Figure

The

relationship

between

low-level

and

high-level

microarray

analysis

ples

-LSB-

-RSB-

tioners

misdiagnoses

-LSB-

-RSB-

The

common

thread

among

these

high-level

microarray

analysis

problems

is

that

they

answer

sophisticated

questions

of

direct

biological

interest

to

medical

researchers

-LRB-

such

as

which

genes

are

being

co-expressed

under

treatment

X

-RRB-

where

the

raw

data

used

are

estimates

of

biologically

meaningful

parameters

-LRB-

such

as

the

expression

level

estimates

for

thousands

of

genes

-RRB-

In

contrast

to

these

so-called

high-level

problems

low-level

microarray

analysis

-LSB-

-RSB-

is

concerned

with

the

preceding

step

in

the

microarray

assay

cycle

-LRB-

Figure

-RRB-

given

raw

data

straight

from

a

scanner

which

has

no

direct

biological

interpretation

clean

and

summarize

this

data

to

produce

the

biologically

meaningful

parameter

estimates

-LRB-

such

as

expression

level

estimates

-RRB-

that

are

later

used

in

high-level

analyses

In

low-level

analysis

more

consideration

is

generally

given

to

the

behavior

of

the

underlying

molecular

biology

microarray

technology

and

experimental

design

than

in

high-level

analysis

This

makes

generative

methods

readily

applicable

in

low-level

problems

facilitating

the

formulation

of

confidence

SIGKDD

Explorations

and

even

identifying

medical

practi-Volume

Issue

Page

statements

such

as

p-values

in

gene

expression

calls

Hence

while

high-level

problems

have

been

tackled

with

discriminative

approaches

such

as

those

found

in

machine

learning

and

data

mining

in

addition

to

classical

statistical

methods

the

low-level

analysis

community

has

traditionally

called

upon

only

the

latter

In

this

paper

we

argue

that

low-level

microarray

analysis

poses

a

number

of

interesting

problems

for

the

data

mining

and

machine

learning

community

distinct

to

the

traditional

high-level

microarray

problems

These

problems

are

relevant

to

the

long-term

success

of

DNA

microarrays

and

are

already

topics

of

active

research

in

the

low-level

microarray

analysis

community

It

is

our

hope

that

this

position

paper

motivates

and

enables

further

machine

learning

research

in

the

area

Although

we

will

focus

on

high

density

oligonucleotide

microarrays

particularly

those

of

the

Affymetrix

GeneChip

variety

the

underlying

concepts

and

opportunities

remain

the

same

for

related

technologies

Throughout

the

paper

we

distinguish

machine

learning

from

statistics

While

these

disciplines

are

closely

related

and

serve

as

foundations

for

inference

in

microarray

analysis

the

distinction

does

have

content

In

our

view

classical

statistics

is

generative

dealing

with

relatively

low-dimensional

data

and

parameter

spaces

while

machine

learning

is

often

discriminative

in

nature

and

explicitly

addresses

computational

issues

in

high-dimensional

data

analysis

Section

reviews

relevant

background

ideas

from

machine

learning

For

an

overview

of

the

background

molecular

biology

and

microarray

technology

see

the

guest

editorial

elsewhere

in

this

issue

The

low-level

problems

of

absolute

and

differential

expression

level

summarization

expression

detection

and

transcript

discovery

are

reviewed

in

Section

along

with

suggested

applications

of

machine

learning

approaches

to

these

problems

Sections

and

similarly

cover

microarray-based

genotyping

and

re-sequencing

Finally

Section

concludes

the

paper

We

assume

familiarity

with

the

notions

of

unsupervised

learning

-LRB-

clustering

-RRB-

and

supervised

learning

-LRB-

classification

and

regression

-RRB-

As

many

of

the

low-level

analysis

problems

discussed

below

are

amenable

to

learning

from

partially

labeled

data

learning

from

heterogeneous

data

and

incremental

learning

we

briefly

review

these

paradigms

here

Learning

from

Partially

Labeled

Data

Given

an

i.i.d.

labeled

sample

-LCB-

-LRB-

x

i

y

i

-RRB-

-RCB-

n

i

drawn

from

the

unknown

and

fixed

joint

distribution

F

-LRB-

x

y

-RRB-

and

an

i.i.d.

unlabeled

sample

-LCB-

x

i

-RCB-

m

i

n

drawn

from

the

marginal

distribution

F

-LRB-

x

-RRB-

the

problem

of

learning

from

partially

labeled

data

-LSB-

-RSB-

is

to

use

the

data

in

choosing

a

function

g

m

-LRB-

X

-RRB-

approximating

E

-LRB-

Y

X

-RRB-

where

-LRB-

X

Y

-RRB-

F

This

problem

has

been

motivated

by

a

number

of

applications

where

only

limited

labeled

data

is

present

say

due

to

expense

while

unlabeled

data

is

plentiful

-LSB-

-RSB-

This

is

particularly

the

case

in

the

areas

of

text

classification

medical

research

and

computer

vision

-LSB-

-RSB-

within

which

much

of

the

research

into

learning

from

partially

labeled

data

has

occurred

This

problem

also

called

the

labeled-unlabeled

data

problem

-LSB-

-RSB-

has

been

explored

under

a

number

of

closely-related

guises

Some

of

the

earliest

approaches

used

so-called

hybrid

learners

-LSB-

-RSB-

where

an

unsupervised

learning

algorithm

assigns

labels

to

the

unlabeled

data

thereby

expanding

the

labeled

dataset

for

subsequent

supervised

learning

The

term

multimodal

learning

is

sometimes

used

to

refer

to

partially

labeled

learning

in

the

computer

vision

literature

-LSB-

-RSB-

Co-training

is

a

form

of

partially

labeled

learning

where

the

two

datasets

may

be

of

different

types

and

one

proceeds

by

using

the

unlabeled

data

to

bootstrap

weak

learners

trained

on

the

labeled

data

-LSB-

-RSB-

More

recently

semi-supervised

learning

-LSB-

-RSB-

and

transductive

learning

-LSB-

-RSB-

have

gained

popularity

Equivalent

to

partially

labeled

learning

semi-supervised

learning

includes

a

number

of

successful

algorithms

such

as

those

based

on

the

support

vector

machine

-LRB-

SVM

-RRB-

-LSB-

-RSB-

Transductive

learners

on

the

other

hand

aim

to

predict

labels

for

just

the

unlabeled

data

at

hand

without

producing

the

inductive

approximation

g

m

This

approach

can

be

used

to

generalize

the

aforementioned

hybrid

learners

whose

unsupervised

step

typically

ignores

the

labeled

data

In

particular

it

is

shown

in

-LSB-

-RSB-

that

direct

transduction

is

more

effective

than

the

traditional

two-step

approach

of

induction

followed

by

deduction

A

number

of

transductive

schemes

have

been

proposed

such

as

those

based

on

the

SVM

-LSB-

-RSB-

a

graph-based

transductive

learner

-LSB-

-RSB-

and

a

leave-one-out

error

ridge

regression

method

-LSB-

-RSB-

Joachims

-LSB-

-RSB-

describes

an

approximate

solver

for

the

semi-supervised

SVM

which

utilizes

a

fast

SVM

optimizer

as

an

inner

loop

The

story

is

not

all

good

-LSB-

-RSB-

tells

us

that

while

unlabeled

data

may

be

useful

labeled

examples

are

exponentially

more

valuable

in

a

suitable

sense

-LSB-

-RSB-

tells

us

that

unlabeled

data

may

lead

the

transductive

SVM

to

maximize

the

wrong

margin

and

in

-LSB-

-RSB-

it

is

shown

that

unlabeled

data

may

in

fact

degrade

classifier

performance

under

certain

conditions

relating

the

risk

and

empirical

risk

Nonetheless

learning

from

partially

labeled

data

has

enjoyed

great

success

in

many

theoretical

and

empirical

studies

-LSB-

-RSB-

We

are

especially

interested

in

partially

labeled

learning

as

an

approach

to

the

low-level

microarray

analysis

problems

discussed

in

Sections

where

we

have

relatively

few

labeled

examples

but

an

abundant

source

of

unlabeled

data

-LSB-

-RSB-

is

a

recent

example

of

partially

labeled

learning

applied

to

high-level

microarray

analysis

There

the

problem

of

predicting

gene

function

is

tackled

using

a

semi-supervised

scheme

trained

on

a

two-component

dataset

of

DNA

microarray

expression

profiles

and

phylogenetic

profiles

from

whole-genome

sequence

comparisons

This

leads

us

to

the

next

relevant

idea

from

machine

learning

Learning

from

Heterogeneous

Data

Learning

from

heterogeneous

data

is

the

process

of

learning

from

training

data

labeled

or

not

that

can

be

partitioned

into

subsets

each

of

which

contains

a

different

type

of

data

structure

or

originates

from

a

different

source

This

notion

is

equivalent

to

the

methods

of

data

fusion

-LSB-

-RSB-

Research

into

learning

from

heterogeneous

data

tends

to

be

quite

domain-specific

and

has

enjoyed

increasing

interest

from

the

bioinformatics

community

in

particular

-LRB-

e.g.

-LSB-

-RSB-

-RRB-

-LSB-

-RSB-

presents

a

kernel-based

framework

for

learning

from

heterogeneous

descriptions

of

a

collection

of

genes

proteins

or

other

entities

The

authors

demonstrate

the

method

's

superiority

to

the

homogeneous

case

on

the

problem

of

predicting

yeast

protein

function

using

knowledge

of

amino

acid

sequence

protein

complex

data

gene

expression

data

and

known

protein-protein

interactions

SIGKDD

Explorations

Volume

Issue

Page

-LSB-

-RSB-

proposes

an

SVM

method

for

classifying

gene

function

from

microarray

expression

estimates

and

phylogenetic

profiles

This

is

achieved

through

the

construction

of

an

explicitly

heterogeneous

kernel

first

separate

kernels

are

constructed

for

each

data

type

taking

into

account

high-order

within-type

correlations

then

these

kernels

are

combined

ignoring

high-order

across-type

correlations

Our

interest

in

learning

from

heterogeneous

data

arises

because

several

sources

of

knowledge

relevant

to

low-level

microarray

analysis

are

available

and

incorporating

such

problem

domain

knowledge

has

been

shown

to

improve

the

performance

of

learning

algorithms

in

the

past

Incremental

Learning

Incremental

learning

is

focused

on

learning

from

data

presented

sequentially

where

the

model

may

be

required

to

make

predictions

on

unseen

data

during

training

This

is

in

contrast

to

cases

where

all

training

occurs

before

any

predictions

are

made

-LRB-

batch

learning

-RRB-

and

is

similar

to

online

learning

-LSB-

-RSB-

A

number

of

incremental

learning

algorithms

have

been

proposed

and

applied

in

the

literature

For

example

several

incremental

support

vector

machines

have

been

studied

-LSB-

-RSB-

In

-LSB-

-RSB-

incremental

learning

is

applied

to

distributed

video

surveillance

SVM

algorithm

parameter

selection

is

investigated

in

-LSB-

-RSB-

-LSB-

-RSB-

applies

an

incremental

SVM

to

detecting

concept

drift

the

problem

of

varying

distributions

over

long

periods

of

data

gathering

and

to

adaptive

classification

of

documents

with

respect

to

user

interest

An

exact

incremental

SVM

is

proposed

in

-LSB-

-RSB-

where

decremental

unlearning

of

incremental

training

data

is

possible

This

can

be

used

to

efficiently

evaluate

the

computationally-expensive

leave-one-out

error

measure

Due

to

the

relatively

small

sizes

of

datasets

typically

available

in

low-level

microarray

analysis

there

is

great

potential

for

learners

that

can

incrementally

incorporate

new

data

gathered

in

the

lab

thereby

improving

estimator

performance

specific

to

that

lab

's

patterns

of

microarray

assay

The

most

successful

application

of

DNA

microarray

technology

to

date

has

been

to

gene

expression

analysis

Tra-ditionally

this

has

involved

estimating

gene

expression

levels

-LRB-

Section

-RRB-

an

area

that

is

being

addressed

through

successful

statistical

methods

and

active

statistics

research

However

the

task

of

determining

transcription

activity

over

entire

chromosomes

-LRB-

Section

-RRB-

is

less

well

developed

and

offers

serious

opportunities

for

machine

learning

Gene

Expression

Monitoring

The

Problem

Traditional

microarrays

measure

mRNA

target

abundance

using

the

scanned

intensities

of

fluorescence

from

tagged

molecules

hybridized

to

substrate-attached

probes

-LSB-

-RSB-

The

brighter

the

intensity

within

a

cell

of

identical

probes

the

more

hybridization

there

has

been

to

those

probes

-LRB-

Figure

-RRB-

The

scanned

intensity

then

roughly

corresponds

to

target

abundance

Since

probes

are

limited

in

length

while

targets

may

be

thousands

of

bases

long

the

GeneChip

uses

a

set

of

probes

to

detect

each

target

nucleic

acid

The

probes

are

spread

out

Figure

Probe-level

features

for

expression

level

summarization

-LRB-

a

-RRB-

a

cell

of

probes

-LRB-

b

-RRB-

target

transcript

perfect

match

probe

and

mis-match

probe

sequences

and

-LRB-

c

-RRB-

scanned

and

image-analyzed

probe-level

intensities

along

a

base

pair

region

close

to

the

end

of

the

transcript

To

measure

the

effects

of

cross-hybridization

or

un-intended

hybridization

of

target

A

to

the

probes

intended

for

target

B

a

system

of

probe

pairs

is

used

In

each

pair

a

perfect

match

-LRB-

PM

-RRB-

probe

contains

the

target

's

exact

complementary

sequence

while

a

mismatch

-LRB-

MM

-RRB-

probe

replaces

the

middle

base

of

the

perfect

match

probe

with

its

Watson-Crick

complement

In

this

way

a

target

is

probed

by

a

probe

set

of

PM-MM

probe

pairs

The

aim

is

roughly

for

the

PMs

to

measure

signal

plus

noise

and

for

the

MMs

to

measure

just

noise

so

that

the

signal

is

revealed

using

some

function

of

-LRB-

PM

MM

-RRB-

Figure

depicts

the

probe

set

arrangement

while

Figure

gives

an

example

of

the

scanned

intensities

We

may

now

define

the

expression

level

summarization

problem

Low-level

Problem

Given

a

probe

set

's

intensities

-LRB-

possibly

after

background

correction

and

normalization

-RRB-

the

expression

level

summarization

problem

is

to

estimate

the

amount

of

target

transcript

present

in

the

sample

While

the

expression

level

summary

aims

to

estimate

gene

expression

level

from

the

features

of

Figure

expression

detection

is

concerned

with

determining

the

presence

of

any

gene

expression

at

all

Low-level

Problem

Given

a

probe

set

's

intensities

possibly

normalized

the

expression

detection

problem

is

to

predict

whether

the

target

transcript

is

present

-LRB-

P

-RRB-

or

absent

-LRB-

A

-RRB-

in

the

sample

or

otherwise

call

marginal

-LRB-

M

-RRB-

if

it

is

too

difficult

to

tell

In

addition

to

the

P/M/A

detection

call

we

wish

to

state

a

confidence

level

in

the

call

such

as

a

p-value

Detection

calls

are

not

as

widely

utilized

as

expression

level

estimates

They

are

often

used

for

example

to

filter

out

genes

with

negligible

expression

before

performing

computationally

expensive

high-level

analyses

such

as

clustering

on

gene

expression

profiles

SIGKDD

Explorations

Volume

Issue

Page

The

previous

two

problems

dealt

with

estimates

based

on

a

single

probe-set

read

from

a

single

array

Comparative

studies

on

the

other

hand

involve

assaying

two

arrays

one

the

baseline

and

the

other

the

experiment

followed

by

computation

of

a

single

comparative

estimate

Low-level

Problem

Given

two

sets

of

intensities

possibly

normalized

for

the

same

probe

set

on

two

arrays

a

The

differential

expression

level

summarization

problem

is

to

estimate

the

relative

abundance

of

target

transcript

on

each

array

b

The

comparison

call

problem

is

to

predict

whether

the

expression

of

the

target

has

increased

not

changed

or

decreased

from

one

chip

to

the

other

As

in

Low-level

Problem

a

statement

of

confidence

in

the

call

should

be

supplied

The

log-ratio

of

expression

levels

for

a

target

is

sometimes

known

as

the

relative

expression

level

-LSB-

-RSB-

and

is

closely

related

to

the

notion

of

fold

change

-LRB-

which

is

sign

-LRB-

log-ratio

-RRB-

-RRB-

Comparison

calls

are

sometimes

referred

to

as

change

calls

An

advantage

of

working

with

these

comparative

estimates

is

that

probe-specific

affinities

-LRB-

one

cause

of

undesired

variation

-RRB-

are

approximately

cancelled

out

by

taking

ratios

-LSB-

-RSB-

All

of

these

problems

are

complicated

by

exogenous

sources

of

variation

which

cloud

the

quantities

we

are

interested

in

-LSB-

-RSB-

proposes

a

breakdown

of

the

sources

of

variation

in

microarray

experiments

into

intrinsic

noise

-LRB-

variation

inherent

in

the

experiment

's

subjects

-RRB-

intermediate

noise

-LRB-

arising

for

example

from

laboratory

procedures

-RRB-

and

measurement

error

-LRB-

variation

due

to

the

instrumentation

such

as

array

manufacture

scanning

or

in

silico

processing

-RRB-

Current

Approaches

At

the

level

of

microarray

design

sophisticated

probe

modeling

and

combinatorial

techniques

are

used

to

reduce

probe-specific

effects

and

cross-hybridization

However

much

of

the

unwanted

variation

identified

above

must

still

be

tackled

during

low-level

analysis

This

means

that

care

must

be

taken

with

the

relevant

statistical

issues

For

example

in

experimental

design

we

must

trade

off

between

biological

replicates

-LRB-

across

samples

-RRB-

and

technical

replicates

-LRB-

one

sample

across

chips

-RRB-

Background

correction

and

normalization

for

reducing

systematic

variation

within

and

across

replicate

arrays

also

surface

as

major

considerations

-LSB-

-RSB-

Three

popular

approaches

to

Low-level

Problem

-LSB-

-RSB-

are

the

Affymetrix

microarray

suite

-LRB-

MAS

-RRB-

signal

measure

-LSB-

-RSB-

the

robust

multi-array

average

-LRB-

RMA

-RRB-

-LSB-

-RSB-

and

the

model-based

expression

index

-LRB-

MBEI

-RRB-

-LSB-

-RSB-

first

performs

background

correction

by

subtracting

a

background

estimate

for

each

cell

computed

by

partitioning

the

array

into

rectangular

zones

and

setting

the

background

of

each

zone

to

that

zone

's

second-percentile

intensity

Next

subtracts

an

ideal

mismatch

value

from

each

PM

intensity

and

log-transforms

the

adjusted

PMs

to

stabilize

the

variance

A

robust

mean

is

computed

for

the

resulting

values

using

a

biweight

estimator

and

finally

this

value

is

scaled

using

a

trimmed

mean

to

produce

the

signal

estimate

RMA

proceeds

by

first

performing

quantile

normalization

-LSB-

-RSB-

which

puts

the

probe

intensity

distributions

across

replicate

arrays

on

the

same

scale

RMA

then

models

the

PMs

Figure

An

ROC

curve

-LRB-

-RRB-

and

-LRB-

-RRB-

correspond

to

the

always

negative

and

always

positive

classifiers

respectively

The

closer

to

the

ideal

point

-LRB-

-RRB-

the

better

Neither

of

the

two

families

A

or

B

dominates

the

other

Instead

one

or

the

other

is

better

according

to

the

desired

trade-off

between

FP

and

TP

as

background

plus

signal

where

the

signal

is

exponentially

and

the

background

normally

distributed

MM

intensities

are

not

used

in

RMA

A

robust

additive

model

is

used

to

model

the

PM

signal

-LRB-

in

log-space

-RRB-

as

the

sum

of

the

log

scale

expression

level

a

probe

affinity

effect

and

an

i.i.d.

error

term

Finally

median

polish

estimates

the

model

parameters

and

produces

the

log-scale

expression

level

summary

MBEI

fits

P

M

i

j

M

M

i

j

i

j

i

j

using

maximum

likelihood

to

estimate

the

per-gene

expression

levels

i

Here

the

j

are

probe-specific

affinities

and

the

i

j

are

i.i.d.

normal

errors

Although

it

may

seem

that

expression

detection

is

just

a

matter

of

thresholding

expression

level

estimates

this

has

proven

not

to

be

the

case

-LSB-

-RSB-

It

is

known

that

expression

level

estimators

often

have

difficulty

at

low

levels

of

expression

while

detection

algorithms

are

designed

with

this

setting

in

mind

The

most

widely

used

detection

algorithm

for

the

GeneChip

is

a

method

based

on

a

Wilcoxon

signed-rank

test

-LSB-

-RSB-

This

algorithm

corresponds

to

a

hypothesis

test

of

H

median

-LRB-

P

M

i

M

M

i

P

M

i

M

M

i

-RRB-

versus

H

median

-LRB-

P

M

i

M

M

i

P

M

i

M

M

i

-RRB-

where

is

a

small

positive

constant

These

hypotheses

correspond

to

absence

and

presence

of

expression

respectively

The

test

is

conducted

using

a

p-value

for

a

sum

of

signed

ranks

R

i

P

M

i

M

M

i

P

M

i

M

M

i

The

p-value

is

thresholded

so

that

values

in

-LSB-

-RRB-

-LSB-

-RRB-

and

-LSB-

-RSB-

result

in

present

marginal

and

absent

calls

respectively

Here

control

the

trade-off

between

false

positives

-LRB-

FP

-RRB-

and

true

positives

-LRB-

TP

-RRB-

Recently

a

number

of

alternate

rank

sum-based

algorithms

have

been

proposed

-LSB-

-RSB-

One

in

particular

a

variant

on

the

method

where

scores

are

set

to

R

i

log

P

M

i

M

M

i

has

been

shown

to

outperform

detection

in

a

range

of

real-world

situations

One

aspect

of

the

study

in

-LSB-

-RSB-

of

particular

interest

is

the

use

of

the

Receiver

Operating

Characteristic

-LRB-

ROC

-RRB-

Convex

Hull

method

-LSB-

-RSB-

for

comparing

competing

classifiers

on

a

spike-in

test

set

ROC

curves

-LRB-

see

Figure

-RRB-

characterize

the

classification

performance

of

a

family

of

classifiers

parameterized

by

a

tun-SIGKDD

Explorations

Volume

Issue

Page

able

parameter

that

controls

the

FP-TP

trade-off

For

example

as

the

level

of

a

hypothesis

test

is

decreased

the

rate

of

false

positive

rejections

decreases

-LRB-

by

definition

-RRB-

while

the

rate

of

false

negative

acceptances

will

typically

go

up

An

ROC

curve

encodes

this

trade-off

extending

the

notion

of

contingency

table

to

an

entire

curve

It

is

a

more

expressive

object

than

accuracy

which

boils

performance

down

to

one

number

-LSB-

-RSB-

Comparing

ROC

curves

has

traditionally

been

achieved

by

either

choosing

the

clear

winner

-LRB-

in

the

rare

case

of

domination

-LSB-

-RSB-

-RRB-

or

choosing

the

maximizer

of

the

Area

Under

Curve

-LRB-

AUC

-RRB-

Although

AUC

works

in

some

cases

it

gives

equal

credit

to

performance

over

all

misclassification

cost

and

class

size

settings

usually

an

undesirable

strategy

if

any

domain

knowledge

is

available

The

ROC

Convex

Hull

method

on

the

other

hand

relates

expected-cost

optimality

to

conditions

on

relative

misclassification

cost

and

class

size

so

that

the

typical

case

of

semi-dominance

-LRB-

as

in

Figure

-RRB-

can

be

handled

in

a

principled

way

rather

than

selecting

p-value

thresholds

by

hand

end-users

are

provided

with

the

right

classifier

and

thresholds

by

the

method

This

use

of

the

ROCCH

method

demonstrates

a

surprising

application

of

machine

learning

to

low-level

microarray

analysis

Many

of

these

absolute

expression

algorithms

have

their

comparative

analogues

For

example

produces

the

signal

log

ratio

with

an

associated

confidence

interval

using

a

biweight

algorithm

-LSB-

-RSB-

also

implements

a

comparison

call

based

on

the

Wilcoxon

signed-rank

sum

test

just

as

in

the

absolute

detection

algorithm

above

-LSB-

-RSB-

While

the

Affymetrix

microarray

suite

is

the

software

package

bundled

with

the

GeneChip

the

Bioconductor

project

-LSB-

-RSB-

an

open-source

set

of

R

-LSB-

-RSB-

packages

for

bioinformatics

data

analysis

has

been

gaining

popularity

and

implements

most

of

the

methods

discussed

here

Open

Problems

While

Low-level

Problem

involves

prediction

of

continuous

expression

levels

-LRB-

non-negative

real

values

-RRB-

given

a

vector

of

-LRB-

non-negative

real

-RRB-

perfect

match

and

mismatch

intensities

with

total

length

between

and

Low-level

Problem

is

a

classification

problem

with

call

confidence

levels

Open

Problem

In

the

respective

settings

of

Low-level

Problems

a

What

machine

learning

techniques

are

competitive

with

algorithms

based

on

classical

statistical

methods

for

expression

level

estimation

b.

Which

machine

learning

classifiers

are

competitive

for

expression

detection

c

What

machine

learning

methods

achieve

high

performance

on

the

comparative

analogues

of

the

previous

two

problems

posed

on

the

appropriate

product

space

of

microarray

measurements

Comparisons

for

expression

level

estimators

might

be

made

based

on

bias

and

variance

computational

efficiency

and

biological

relevance

of

learned

models

The

ROCCH

method

is

ideal

for

detector

comparison

Issues

of

background

correction

and

normalization

across

multiple

arrays

must

likely

also

be

addressed

to

enable

competitiveness

with

the

state

of

the

art

Research

into

applying

semi-supervised

heterogeneous

data

and

incremental

learners

to

gene

expression

monitoring

is

directly

motivated

by

the

proportion

of

labeled

to

unlabeled

data

available

the

existence

of

GeneChip

domain

knowledge

and

the

endemic

nature

of

microarray

assays

that

are

continually

performed

in

individual

research

labs

Biologists

could

augment

the

limited

labeled

probe-level

data

available

with

relatively

abundant

unlabeled

data

Labeled

data

can

be

procured

for

example

from

bacterial

control

experiments

with

known

concentrations

called

spike-in

assays

and

bacterial

control

probe

sets

that

are

present

in

some

GeneChips

for

calibration

purposes

The

former

source

of

labeled

data

is

the

more

useful

for

this

problem

as

it

provides

examples

with

a

range

of

labels

Unfortunately

spike-in

studies

are

rare

because

they

are

not

of

independent

scientific

interest

they

are

only

performed

for

low-level

microarray

research

For

the

few

spike-in

assays

that

are

available

only

a

small

number

of

targets

are

spiked

in

at

an

equally

small

number

of

concentrations

-LRB-

typically

-RRB-

Unlabeled

data

in

contrast

could

be

taken

from

the

large

collection

of

available

biologically

relevant

assays

each

one

providing

tens

of

thousands

of

data

points

Beyond

probe

intensities

other

data

sources

could

include

probe

sequences

and

probe-affinity

information

derived

from

probe

models

Such

information

is

closely

related

to

the

hybridization

process

and

might

be

of

use

in

expression

level

estimation

both

target

and

non-specific

hybridization

are

known

to

be

probe-dependent

Although

labeled

data

from

spike-in

studies

are

of

greatest

utility

for

learning

-LSB-

-RSB-

the

quantity

of

unlabeled

data

produced

by

a

series

of

biologically

interesting

microarray

assays

in

any

given

lab

suggests

a

semi-supervised

incremental

approach

Since

the

ROCCH

involves

taking

a

pointwise

maximum

over

the

individual

noisy

ROC

curves

it

incorporates

a

possibly

large

degree

of

uncertainty

It

should

be

possible

to

extend

the

results

of

-LSB-

-RSB-

to

quantify

this

property

Open

Problem

Can

the

ROC

Convex

Hull

method

of

-LSB-

-RSB-

be

extended

to

provide

confidence

intervals

for

its

conditions

on

expected-cost

optimality

Transcript

Discovery

The

Problem

The

applications

to

expression

monitoring

described

above

are

all

related

to

addressing

questions

about

pre-defined

transcripts

More

precisely

the

vast

majority

of

expression

analysis

is

performed

using

probes

interrogating

only

a

small

sub-sequence

of

each

transcript

This

has

clearly

been

a

useful

approach

but

there

are

at

least

two

potential

drawbacks

One

is

that

we

can

only

monitor

the

expression

of

genes

known

to

exist

at

the

time

of

the

array

's

design

Even

in

a

genome

as

well-studied

as

that

of

the

human

new

transcripts

are

routinely

discovered

Another

is

that

in

directly

monitoring

only

a

sub-sequence

of

the

transcript

it

will

often

be

impossible

to

distinguish

between

alternatively

spliced

forms

of

the

same

gene

-LRB-

which

may

have

very

different

functional

roles

-RRB-

An

alternative

approach

is

to

use

arrays

with

probes

tiled

uniformly

across

genomic

sequence

without

regard

to

current

knowledge

of

transcription

Such

genome

tiling

arrays

have

been

used

to

monitor

expression

in

all

the

non-repetitive

sequence

of

human

chromosomes

and

-LSB-

-RSB-

and

more

widespread

use

is

underway

SIGKDD

Explorations

Volume

Issue

Page

The

problems

arising

in

the

analysis

of

data

from

genome

tiling

arrays

are

essentially

the

same

as

those

for

the

expression

monitoring

arrays

described

above

estimation

of

expression

level

detection

of

presence

and

detection

of

differential

expression

There

is

however

the

additional

challenge

of

determining

the

number

of

distinct

transcripts

and

their

location

within

the

tiled

genomic

region

Low-level

Problem

The

problem

of

transcript

discovery

can

be

viewed

in

two

steps

a.

Determining

the

exon

structure

of

genes

within

a

tiled

region

and

b.

Determining

which

exons

should

be

classified

together

as

part

of

a

single

gene

's

transcript

Current

Approaches

A

simple

heuristic

approach

is

taken

in

-LSB-

-RSB-

in

which

PM-MM

probe

pairs

are

classified

as

positive

or

negative

based

on

thresholds

applied

to

the

difference

and

ratio

of

the

PM

and

MM

values

Positions

classified

as

positive

and

located

close

to

other

positive

positions

are

grouped

together

to

form

predicted

exons

A

more

effective

approach

-LSB-

-RSB-

is

based

on

the

application

of

a

Wilcoxon

signed-rank

test

in

a

sliding

window

along

the

genomic

sequence

using

the

associated

Hodges-Lehmann

estimator

for

estimation

of

expression

level

Grouping

into

exons

is

achieved

by

thresholding

on

present

call

p-values

or

estimated

expression

level

then

defining

groups

of

probes

exceeding

the

threshold

to

be

exons

Open

Problems

The

problem

of

detecting

exons

based

on

probe

intensities

-LRB-

Low-level

Problem

-RRB-

is

very

similar

to

the

problem

of

absolute

expression

detection

-LRB-

Low-level

Problem

-RRB-

For

example

the

exon

detection

method

of

-LSB-

-RSB-

and

the

expression

detection

algorithm

-LSB-

-RSB-

are

both

built

around

the

Wilcoxon

signed-rank

test

The

problem

of

finding

exons

has

been

addressed

as

described

but

the

methods

are

heuristic

and

there

is

plenty

of

room

for

improvement

Associating

exons

to

form

transcripts

-LRB-

Low-level

Problem

-RRB-

has

been

addressed

in

a

large

experiment

across

almost

experimental

pairs

using

a

heuristic

correlation-based

method

again

this

presents

an

opportunity

for

research

into

more

effective

methods

Open

Problem

Are

there

machine

learning

methods

that

are

able

to

out-perform

current

classical

statistical

methods

in

transcript

discovery

as

defined

in

Low-Level

Problem

One

possibility

which

appears

well

suited

to

the

problem

is

the

use

of

hidden

Markov

models

where

the

underlying

un-observed

Markov

chain

is

over

states

representing

expressed

versus

non-expressed

sequence

The

distribution

of

the

observed

probe

intensities

would

depend

on

the

underlying

hidden

state

Another

possible

approach

considering

the

success

which

has

been

demonstrated

in

predicting

genes

from

sequence

data

alone

would

also

be

to

integrate

array-derived

data

with

sequence

information

in

prediction

of

transcripts

Descriptions

of

genome

sequencing

efforts

such

as

the

human

genome

project

often

lend

the

impression

that

there

is

a

unique

genomic

sequence

associated

with

each

species

This

is

a

useful

and

approximately

correct

abstraction

But

in

fact

any

two

individuals

picked

at

random

from

a

species

population

will

have

differing

nucleotides

at

a

small

fraction

of

the

corresponding

positions

in

their

genomes

Such

single-nucleotide

polymorphisms

or

SNPs

help

form

the

basis

of

genetically-determined

variation

across

individuals

Biologists

estimate

that

about

one

position

in

in

the

human

genome

is

a

SNP

With

over

billion

bases

of

genomic

DNA

we

see

that

SNPs

number

in

the

several

millions

Although

there

are

other

kinds

of

individual

genomic

variation

such

as

insertions

deletions

and

duplications

of

DNA

segments

our

focus

here

is

SNPs

Further

complicating

the

picture

is

the

fact

that

humans

are

diploid

organisms

each

person

possesses

two

complete

but

different

copies

of

the

human

genome

one

inherited

from

the

mother

and

one

from

the

father

Now

consider

a

polymorphic

position

or

locus

at

which

two

different

bases

occur

in

the

population

say

G

and

T

These

variants

are

called

the

alleles

at

the

locus

so

in

this

case

we

are

describing

a

biallelic

SNP

A

given

individual

will

have

inherited

either

a

G

or

T

in

the

paternal

genome

and

the

same

is

true

of

the

maternal

genome

Thus

there

are

three

possible

genotypes

or

individual

genetic

signatures

at

this

SNP

they

are

de-noted

GG

TT

and

GT

We

do

not

distinguish

the

last

case

from

TG

since

there

is

no

inherent

ordering

of

the

paternal

and

maternal

genomes

at

a

given

polymorphic

position

We

refer

generically

to

the

alleles

of

a

biallelic

SNP

as

A

and

B.

Biological

evidence

suggests

that

essentially

all

SNPs

are

biallelic

in

humans

The

genotyping

problem

then

is

to

establish

an

individual

's

genotype

as

AA

BB

or

AB

for

as

many

SNPs

as

possible

in

the

human

genome

The

completion

of

the

human

genome

project

means

that

one

has

recourse

to

the

full

genomic

sequence

surrounding

a

SNP

to

help

solve

the

genotyping

problem

Furthermore

various

large-scale

public

projects

to

locate

SNPs

and

identify

their

alleles

exist

notably

The

SNP

Consortium

-LRB-

TSC

-RRB-

the

data

they

generate

may

also

be

utilized

for

genotyping

The

major

drawback

to

traditional

genotyping

protocols

are

their

lack

of

parallelism

with

consequent

expense

in

terms

of

material

and

labor

In

contrast

Kennedy

et

al.

-LSB-

-RSB-

describe

whole-genome

sampling

analysis

-LRB-

WGSA

-RRB-

which

enables

massively

parallel

genotyping

via

genotyping

microarrays

For

the

Affymetrix

Mapping

Array

which

genotypes

approximately

SNPs

across

the

human

genome

each

SNP

actually

has

corresponding

probes

collectively

termed

a

miniblock

The

miniblock

has

probe

quartets

for

the

SNP

's

flanking

region

on

the

forward

strand

and

another

probe

quartets

for

the

reverse

complement

strand

so

yields

probes

Each

probe

quartet

in

turn

corresponds

to

a

in

which

the

SNP

is

at

one

of

offsets

from

the

central

position

The

four

probes

within

a

probe

quartet

differ

in

the

base

they

put

at

the

SNP

a

perfect

match

to

the

A

allele

a

perfect

match

to

the

B

allele

and

mismatches

for

each

Low-level

Problem

Given

a

SNP

's

of

miniblo-SIGKDD

Explorations

Volume

Issue

Page

ck

probe

intensities

the

genotype

calling

problem

is

to

predict

the

individual

's

corresponding

alleles

as

AA

BB

or

AB

Write

PM

-LRB-

A

-RRB-

PM

-LRB-

B

-RRB-

MM

-LRB-

A

-RRB-

and

MM

-LRB-

B

-RRB-

for

the

probe

intensities

within

a

quartet

We

would

then

hope

that

an

AA

individual

has

PM

-LRB-

A

-RRB-

MM

-LRB-

A

-RRB-

but

PM

-LRB-

B

-RRB-

MM

-LRB-

B

-RRB-

for

all

probe

quartets

on

both

strands

For

a

BB

individual

we

hope

to

find

just

the

opposite

effect

and

an

AB

individual

should

have

both

PM

-LRB-

A

-RRB-

MM

-LRB-

A

-RRB-

and

PM

-LRB-

B

-RRB-

MM

-LRB-

B

-RRB-

The

mismatch

probes

in

each

quartet

act

as

controls

establishing

the

level

of

nonspecific

hybridization

for

their

corresponding

perfect

match

probes

The

presence

of

multiple

probe

quartets

allows

for

the

determination

of

genotype

even

when

one

strand

and/or

some

offsets

do

not

yield

reliable

hybridization

say

for

biochemical

reasons

Current

Approaches

Low-level

Problem

is

a

three-class

classification

problem

In

many

machine

learning

applications

the

metric

of

interest

for

competing

classifiers

is

predictive

accuracy

in

this

case

the

probability

of

correctly

genotyping

a

new

individual

's

SNP

based

on

the

miniblock

vector

However

in

the

kinds

of

genetic

studies

which

take

large

numbers

of

genotypes

as

input

there

is

usually

an

explicit

requirement

that

genotype

predictions

have

a

prespecified

accuracy

often

To

attain

such

accuracy

it

is

usually

permissible

for

some

fraction

of

genotypable

SNPs

to

be

no-calls

that

is

the

classifier

can

refuse

to

predict

a

genotype

for

some

miniblocks

When

comparing

genotypers

our

interest

therefore

lies

in

the

trade-off

between

the

rate

of

no-calls

and

the

accuracy

attained

on

those

SNPs

which

are

called

For

example

some

studies

consider

the

punt

rate

or

lowest

no-call

rate

which

yields

a

prespecified

accuracy

level

on

the

called

SNPs

A

simple

unsupervised

approach

to

training

a

genotyper

is

to

ignore

available

labels

during

training

instead

using

these

labels

to

subsequently

assess

the

trade-off

between

accuracy

and

no-call

rate

for

the

trained

model

This

is

the

strategy

pursued

by

MPAM

-LRB-

modified

partitioning

around

medoids

-RRB-

-LSB-

-RSB-

the

discriminative

clustering

genotyper

used

for

the

Affymetrix

array

An

alternative

approach

using

a

parametric

generative

model

for

the

clustering

will

be

described

elsewhere

It

resembles

ABACUS

a

model

studied

in

the

context

of

re-sequencing

microarrays

-LSB-

-RSB-

-LRB-

see

Section

-RRB-

Open

Problems

Open

Problem

Are

there

machine

learning

methods

that

are

able

to

meet

typical

accuracy

and

punt-rate

specifications

on

the

genotype

calling

problem

In

order

to

choose

a

genotyper

using

supervised

learning

we

need

labels

-LRB-

true

genotypes

-RRB-

along

with

corresponding

miniblock

reads

from

genotyping

arrays

Unfortunately

there

is

no

large-scale

set

of

publicly

available

genotypes

Instead

one

makes

do

with

modestly-sized

sets

of

genotypes

available

commercially

from

companies

using

smaller-scale

techniques

Of

course

no

genotyping

method

is

error-free

so

in

practice

one

measures

concordance

with

reference

genotypes

If

the

concordance

is

high

enough

the

remaining

cases

of

disagreement

between

a

candidate

genotyper

and

the

reference

genotypes

can

be

resolved

via

the

older

labor-intensive

methods

The

incomplete

nature

of

reference

genotype

data

leads

naturally

to

the

setting

of

semi-supervised

learning

Rather

than

falling

back

to

unsupervised

methods

such

as

those

described

above

we

may

consider

employing

more

general

semi-supervised

learners

as

described

in

Section

Additionally

the

methods

of

-LSB-

-RSB-

could

be

used

to

incorporate

low-level

physical

parametric

models

of

hybridization

into

a

kernel-based

classifier

As

explained

in

Section

within

a

single

species

genomic

sequence

will

vary

slightly

from

one

individual

to

the

next

While

Low-level

Problem

focuses

on

the

determination

of

genotype

at

a

position

known

in

advance

to

be

polymorphic

the

problem

described

in

this

section

concerns

locating

such

polymorphic

sites

in

the

first

place

The

usual

starting

point

is

a

newly-sequenced

genome

such

as

the

recently-finished

human

genome

It

is

often

the

case

that

based

on

previous

research

an

investigator

will

be

interested

in

detailed

study

of

variation

in

a

particular

genomic

region

-LRB-

say

on

the

order

of

tens

or

hundreds

of

kilo-bases

-RRB-

and

wants

to

re-sequence

this

region

in

a

large

number

of

individuals

Such

re-sequencing

allows

for

identification

of

the

small

subset

of

polymorphic

locations

Here

we

consider

the

more

recent

challenges

of

microarray-based

re-sequencing

of

diploid

genomic

DNA

A

typical

re-sequencing

array

uses

eight

probes

to

interrogate

each

base

of

the

monitored

sequence

These

eight

probes

comprise

two

quartets

one

for

the

forward

strand

and

one

for

the

reverse

Each

quartet

is

formed

of

probes

perfectly

complementary

to

the

bases

of

the

reference

sequence

centered

on

the

interrogated

base

but

with

all

four

possible

bases

used

at

the

central

position

Low-level

Problem

The

goal

of

the

re-sequencing

problem

is

to

start

with

a

set

of

probe

intensities

and

classify

each

position

as

being

one

of

A

C

G

T

AC

AG

AT

CG

CT

GT

or

N

where

N

represents

a

no

call

-LRB-

due

to

sample

failure

or

ambiguous

data

-RRB-

The

intuition

is

that

for

a

homozygous

position

one

of

the

four

probes

should

be

much

brighter

relative

to

the

others

on

each

strand

and

for

a

heterozygous

position

two

probes

corresponding

to

the

two

bases

of

a

SNP

should

be

brighter

on

each

strand

Of

particular

interest

are

positions

in

which

the

called

base

is

heterozygous

or

homozygous

and

different

to

the

reference

sequence

as

such

positions

exhibit

polymorphism

and

are

candidate

positions

for

explaining

phenotypic

differences

between

individuals

At

face

value

this

classification

problem

is

much

harder

than

the

genotyping

problem

There

are

fewer

probes

to

start

with

-LRB-

a

miniblock

of

rather

than

or

more

-RRB-

and

more

categories

-LRB-

as

opposed

to

or

-RRB-

into

which

to

classify

Current

Approaches

The

most

recent

analysis

of

the

kind

of

re-sequencing

array

discussed

here

-LSB-

-RSB-

is

based

on

modeling

pixel

intensities

within

each

probe

as

independent

random

variables

with

a

common

mean

and

variance

The

model

for

a

homozygous

base

is

that

on

each

strand

the

probe

correspond-three

probes

have

another

The

means

and

variance

are

estimated

by

maximum

likelihood

and

the

likelihood

of

the

SIGKDD

Explorations

ing

to

the

base

has

one

mean

and

variance

and

the

other

Volume

Issue

Page

model

is

evaluated

The

model

for

each

of

the

six

heterozygous

possibilities

is

similar

except

two

probes

correspond

to

each

heterozygote

model

and

the

other

two

are

background

The

likelihoods

-LRB-

overall

and

for

each

strand

-RRB-

are

converted

to

scores

and

provided

the

maximum

score

exceeds

some

threshold

the

best-scoring

model

is

chosen

as

the

base

call

A

number

of

other

filters

that

deal

with

the

signal

absence

signal

saturation

sample

failure

and

so

on

are

applied

as

is

an

iterative

procedure

to

account

for

bias

in

the

background

probes

This

method

called

ABACUS

was

found

to

make

base

calls

at

over

of

all

bases

with

an

estimate

accuracy

in

excess

of

at

the

bases

which

were

called

Open

Problems

A

good

base-calling

method

for

re-sequencing

arrays

already

exists

in

ABACUS

but

there

remains

room

for

improvement

A

recent

and

improved

implementation

-LSB-

-RSB-

of

the

ABACUS

method

on

a

new

genomic

region

found

the

overall

sequencing

accuracy

to

be

on

the

order

of

but

the

accuracy

on

heterozygote

calls

to

be

about

Biologists

would

value

highly

an

improvement

in

heterozygote

call

accuracy

Open

Problem

Can

a

supervised

learning

method

be

used

to

call

bases

in

re-sequencing

arrays

with

accuracy

in

particular

heterozygote

accuracy

in

excess

of

the

accuracies

achieved

by

the

more

classic

statistical

approaches

used

to

date

Considering

the

ongoing

efforts

of

SNP

detection

projects

there

is

an

abundance

of

labeled

data

available

so

the

problem

seems

quite

amenable

to

machine

learning

approaches

As

with

the

genotyping

problem

it

would

be

desirable

to

have

a

measure

of

confidence

associated

with

base

calls

It

may

also

be

useful

to

take

into

account

the

sequences

of

the

probes

as

there

are

known

sequence-specific

effects

on

the

probe

intensities

We

have

described

a

variety

of

low-level

problems

in

microarray

data

analysis

and

suggested

the

applicability

of

methods

from

several

areas

of

machine

learning

Some

properties

of

these

problems

which

should

be

familiar

to

machine

learning

researchers

include

high-dimensional

observations

with

complicated

joint

dependencies

-LRB-

probe

intensities

-RRB-

partially

labeled

data

sets

-LRB-

expression

levels

genotypes

-RRB-

data

from

disparate

domains

-LRB-

microarray

assays

probe

sequences

phylogenetic

information

-RRB-

and

sequential

observations

-LRB-

ongoing

experimental

work

at

individual

labs

-RRB-

We

pointed

out

the

suitability

of

semi-supervised

heterogeneous

and

incremental

learning

in

these

settings

It

is

worth

remarking

that

analogous

problems

arise

with

other

high-throughput

technologies

such

as

cDNA

and

long

oligonucleotide

microarrays

mass

spectrometry

and

fluorescence-activated

cell

sorting

There

are

other

issues

in

low-level

analysis

we

did

not

cover

Here

we

mention

two

of

these

Image

analysis

is

the

problem

of

going

from

raw

pixel

values

in

the

scanned

image

of

a

microarray

to

a

set

of

pixel

intensities

for

each

feature

placed

on

the

probe

and

then

to

single-number

probe

intensities

The

surface

of

the

GeneChip

contains

detectable

grid

points

which

facilitate

rotation

and

translation

of

the

image

to

a

canonical

alignment

subsequent

mapping

of

each

pixel

to

a

feature

is

semi

or

fully

automated

and

has

not

previously

raised

major

analysis

issues

However

work

is

being

done

on

aggressive

reduction

of

feature

sizes

to

a

scale

where

this

mapping

procedure

could

become

a

central

concern

On

the

more

theoretical

side

probe

models

based

on

the

physics

of

polymer

hybridization

have

recently

been

the

focus

of

considerable

interest

These

models

reflect

a

significant

increase

in

the

use

of

biological

knowledge

for

estimating

target

abundance

and

present

an

opportunity

for

application

of

machine

learning

techniques

which

can

exploit

parametric

distributions

in

high-dimensional

data

analysis

such

as

graphical

models

We

close

by

observing

that

a

fuller

awareness

of

low-level

microarray

analysis

issues

will

also

benefit

machine

learning

researchers

involved

with

high-level

problems

the

inevitable

information

reduction

from

earlier

stage

to

later

could

well

conceal

too

much

of

what

the

unfiltered

array

data

reveal

about

the

biological

issue

at

hand

Familiarity

with

initial

normalization

and

analysis

methods

will

allow

the

high-level

analyst

to

account

for

such

a

possibility

when

drawing

scientific

conclusions

We

thank

Rafael

Irizarry

Ben

Bolstad

Francois

Collin

and

Ken

Simpson

for

many

useful

discussions

and

collaboration

on

low-level

microarray

analysis

-LSB-

-RSB-

Affymetrix

Affymetrix

Microarray

Suite

Guide

Affymetrix

Inc.

Santa

Clara

CA

version

-LSB-

-RSB-

M.

Schena

DNA

Microarrays

A

Practical

Approach

Oxford

University

Press

-LSB-

-RSB-

Affymetrix

Statistical

algorithms

description

document

Whitepaper

Affymetrix

Inc.

Santa

Clara

CA

-LSB-

-RSB-

A.

Gammerman

V.

Vovk

and

V.

Vapnik

Learning

by

transduction

In

Fourteenth

Conference

on

Uncertainty

in

Artificial

Intelligence

pages

Morgan

Kaufmann

Publishers

-LSB-

-RSB-

P.

K.

Varshney

Scanning

the

issue

Special

issue

on

data

fusion

Proceedings

of

the

IEEE

-LSB-

-RSB-

R.

O.

Duda

and

P.

E.

Hart

Pattern

Classification

and

Scene

Analysis

John

Wiley

and

Sons

New

York

-LSB-

-RSB-

T.

Gaasterland

and

S.

Bekiranov

Making

the

most

of

microarray

data

Nature

Genetics

-LSB-

-RSB-

K.

P.

Bennett

and

A.

Demiriz

Semi-supervised

support

vector

machines

In

Advances

in

Neural

Information

Processing

Systems

pages

Cambridge

MA

MIT

Press

-LSB-

-RSB-

T.

Joachims

Transductive

learning

via

spectral

graph

partitioning

In

Proceedings

of

the

International

Conference

on

Machine

Learning

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ICML

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V.

Castelli

and

T.

Cover

On

the

exponential

value

of

labeled

samples

Pattern

Recognition

Letters

SIGKDD

Explorations

Volume

Issue

Page

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R.

A.

Irizarry

Science

and

Statistics

A

Festschrift

for

Terry

Speed

volume

of

Lecture

Notes

Monograph

Series

chapter

Measures

of

gene

expression

for

Affymetrix

high

density

oligonucleotide

arrays

pages

Institute

of

Mathematical

Statistics

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R.

Ihaka

and

R.

Gentleman

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A

language

for

data

analysis

and

graphics

Journal

of

Computational

and

Graphical

Statistics

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N.

Friedman

Probabilistic

models

for

identifying

regulation

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M.

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Mei

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L.

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Joint

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K.

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DNA

sequence

variations

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A.

Chakravarti

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genotyping

of

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SNPs

using

generic

high-density

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tag

arrays

Genome

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G.

C.

Kennedy

H.

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Dong

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Liu

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Liu

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Su

M.

Cao

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T.

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Brown

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vector

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expression

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Department

of

Computer

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of

California

at

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Cruz

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P.

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Academy

of

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vector

machine

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for

Biological

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Computational

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Massachusetts

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Tamayo

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Latulippe

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Mesirov

T.

Poggio

W.

Gerald

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Loda

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S.

Lander

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R.

Golub

Multiclass

cancer

diagnosis

using

tumor

gene

expression

signatures

Proceedings

of

the

National

Academy

of

Sciences

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C.

Yeang

S.

Ramaswamy

P.

Tamayo

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Mukherjee

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M.

Rifkin

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Angelo

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Reich

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Design

of

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P.

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methods

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of

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Liu

X.

Di

G.

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H.

Matsuzaki

J.

Huang

R.

Mei

T.

B.

Ryder

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A.

Webster

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Dong

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Liu

K.

W.

Jones

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C.

Kennedy

and

D.

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Algorithms

for

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scale

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microarrays

Bioinformatics

In

press

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-RSB-

Affymetrix

GeneChip

CustomSeq

resequencing

array

Performance

data

for

base

calling

algorithm

in

GeneChip

DNA

analysis

software

Technical

note

Affymetrix

Inc.

Santa

Clara

CA

nical/technotes/customseq

technote.pdf

SIGKDD

Explorations

Volume

Issue

Page

learning from heterogeneous data
statistics
data mining
microarray
transductive learning
heterogeneous data
transcript discovery
incremental learning
genotyping

gene expression estimation
low-level analysis
low-level microarray analysis
re-sequencing
semi-supervised learning


The

Forest

and

the

Trees

Using

Oracle

and

SQL

Server

Together

to

Teach

ANSI-Standard

SQL

Students

in

a

sophomore-level

database

fundamentals

course

were

taught

SQL

and

database

concepts

using

both

Oracle

and

SQL

Server

Previous

offerings

of

the

class

had

used

one

or

the

other

database

Classroom

experiences

suggest

that

students

were

able

to

handle

learning

SQL

in

the

dual

environment

and

in

fact

benefited

from

this

approach

by

better

understanding

ANSI-standard

versus

database-specific

SQL

and

implementation

differences

in

the

two

database

systems

H.

-LSB-

Database

Management

-RSB-

Query

languages

H.

-LSB-

SQL

-RSB-

H.

-LSB-

Microsoft

SQL

Server

-RSB-

H.

-LSB-

Oracle

-RSB-

Languages

A

problem

arises

in

many

technology

classes

The

instructor

wishes

to

teach

principles

and

concepts

of

a

technology

To

give

students

hands-on

experience

putting

those

theories

to

work

a

specific

product

that

implements

that

technology

is

selected

for

a

lab

component

Suddenly

the

students

are

learning

more

about

the

specific

product

than

they

are

about

the

technology

concepts

They

may

or

may

not

realize

what

is

specific

to

that

product

and

what

is

general

to

the

technology

Students

may

even

start

referring

to

the

course

as

a

VB

course

a

PHP

course

or

an

Oracle

course

when

what

you

wanted

to

teach

was

programming

web

scripting

or

database

principles

This

paper

presents

the

experiences

from

a

database

fundamentals

course

that

used

both

Oracle

and

SQL

Server

so

that

students

would

better

understand

ANSI-standard

SQL

Though

each

database

is

ANSI

SQL

compliant

there

are

definite

differences

in

implementation

-LRB-

Gorman

Gulutzan

-RRB-

By

learning

each

implementation

and

how

each

departs

from

ANSI-standard

SQL

students

can

be

better

prepared

to

work

with

any

database

and

better

understand

general

concepts

of

databases

and

SQL

The

paper

discusses

the

observed

results

from

this

approach

and

how

well

the

approach

met

learning

objectives

CPT

Database

Fundamentals

is

a

sophomore-level

database

programming

and

design

class

taught

primarily

to

computer

technology

majors

in

their

fourth

semester

Students

will

have

previously

taken

a

freshman-level

course

that

introduces

them

to

databases

as

a

tool

for

learning

general

information

system

development

terms

and

concepts

The

freshman-level

course

uses

Microsoft

Access

because

it

is

easy

to

use

for

quickly

developing

a

small

personal

information

system

That

course

also

introduces

both

SQL

and

Query

By

Example

methods

for

querying

a

database

as

well

basic

database

design

concepts

which

are

applied

for

simple

data

models

Students

then

move

into

two

programming

courses

the

second

of

which

uses

single-table

SQL

statements

for

providing

data

to

-LRB-

formerly

-RRB-

Visual

Basic

or

-LRB-

currently

-RRB-

web-programming

applications

So

by

the

time

the

students

take

the

Database

Fundamentals

course

they

have

a

concept

of

what

a

database

is

and

how

it

is

used

as

the

back-end

for

programming

The

Database

Fundamentals

course

is

the

course

where

students

learn

SQL

in

depth

database

concepts

and

basic

database

design

It

does

not

teach

stored

procedure

programming

triggers

or

enterprise

or

distributed

database

design

which

are

covered

in

more

advanced

courses

The

learning

objectives

for

the

Database

Fundamentals

course

are

To

understand

the

fundamentals

of

a

relational

database

To

understand

the

fundamentals

of

client-server

and

multi-tiered

applications

To

understand

the

principles

and

characteristics

of

good

relational

database

design

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

or

republish

to

post

on

servers

or

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October

Lafayette

Indiana

USA

Copyright

ACM

To

design

entity

relationship

models

for

a

business

problem

domain

verified

by

the

rules

of

normalization

-LRB-

through

third

normalized

form

-RRB-

To

build

simple

to

moderately

complex

data

models

To

write

simple

to

moderately

complex

SQL

to

query

a

multiple-table

database

To

write

data

manipulation

language

-LRB-

DML

-RRB-

SQL

to

insert

rows

delete

rows

and

update

rows

To

understand

the

concept

of

database

transactions

and

demonstrate

the

proper

use

of

commits

and

rollbacks

To

write

data

definition

language

-LRB-

DDL

-RRB-

SQL

to

create

and

drop

tables

indexes

and

constraints

To

understand

and

be

able

to

implement

the

fundamentals

of

security

and

permissions

in

a

database

To

explain

the

benefits

of

using

views

and

write

SQL

statements

to

create

views

To

create

and

use

SQL

scripts

and

use

SQL

to

build

scripts

To

gain

a

working

knowledge

of

query

optimization

performance

tuning

and

database

administration

To

apply

team

skills

to

build

a

client-server

database

application

CPT

Database

Fundamentals

is

taught

in

a

multi-campus

university

It

was

initially

taught

on

the

main

campus

using

Oracle

When

the

course

was

rolled

out

to

the

regional

campuses

SQL

Server

was

first

used

because

of

administration

considerations

involved

with

Oracle

Now

WAN

connections

have

been

established

that

allow

the

use

of

either

database

engine

or

both

During

the

spring

semester

one

regional

campus

experimented

with

the

use

of

both

databases

The

reasons

for

doing

this

were

To

accomplish

the

course

learning

objectives

in

SQL

necessitates

going

beyond

ANSI-standard

SQL

into

database-specific

functions

sub-queries

and

other

aspects

of

SQL

that

are

implemented

differently

in

different

databases

If

the

students

learn

only

Oracle

or

SQL

Server

-LRB-

or

any

other

database

-RRB-

they

are

likely

to

confuse

ANSI-standard

SQL

with

the

database-specific

implementation

which

can

hinder

them

when

they

enter

the

job

market

By

using

both

databases

it

was

hoped

that

students

would

learn

and

understand

the

differences

among

ANSI-standard

SQL

Oracle

SQL

SQL

Server

T-SQL

Some

design

considerations

such

as

Identities/Sequences

and

datatypes

are

implemented

differently

in

different

databases

Again

students

will

enter

the

job

market

with

a

stronger

understanding

if

they

understand

the

difference

between

the

concept

and

how

it

is

implemented

Neither

Oracle

nor

SQL

Server

commands

a

majority

of

market

share

However

the

two

together

make

up

about

fifty

percent

of

the

current

market

share

positioning

students

well

for

the

job

market

-LRB-

Wong

-RRB-

Studying

two

databases

together

opens

the

door

for

discussing

the

pros

and

cons

of

these

and

other

databases

including

MySQL

and

Sybase

Finally

students

often

want

to

install

a

database

engine

on

their

personal

computer

and

work

on

lab

assignments

at

home

Both

Oracle

and

SQL

Server

have

licensing

and

hardware

requirement

issues

that

on

any

given

computer

may

preclude

one

or

the

other

Using

both

allowed

most

students

to

do

at

least

some

of

their

work

at

home

To

implement

a

cross-engine

approach

an

SQL

text

would

be

needed

that

taught

both

databases

A

special

textbook

was

created

by

two

of

the

instructors

a

draft

of

which

was

used

in

CD

format

In

addition

to

covering

SQL

essentials

in

Oracle

and

SQL

Server

it

also

covered

Microsoft

Access

and

MySQL

in

hopes

that

it

might

also

be

used

in

the

freshman

course

and

be

a

good

reference

for

real

world

web

programming

The

text

has

since

been

picked

up

by

a

publisher

CPT

Database

Fundamentals

consists

of

a

both

a

lecture

and

a

lab

component

The

lectures

cover

fundamental

database

concepts

including

SQL

concepts

query

optimization

and

database

design

and

normalization

The

lab

component

focuses

on

mastery

of

SQL

Table

shows

the

labs

that

were

assigned

and

the

database

used

for

each

Table

Course

Lab

Schedule

Lab

Lab

DBMS

Single

Table

Select

Oracle

Aggregates

Sub

Queries

SQL

Server

Joining

Tables

Both

Oracle

SQL

Server

DBMS

Specific

Functions

Both

Oracle

SQL

Server

Advanced

Queries

Oracle

Data

Manipulation

Student

Choice

Database

Definition

Student

Choice

Privileges

Student

Choice

The

first

three

labs

concentrated

as

much

as

possible

on

ANSI-standard

SQL

Of

course

implementation

of

sub

queries

and

joins

is

in

some

cases

different

between

Oracle

and

SQL

Server

These

differences

were

taught

and

discussed

However

those

labs

avoided

all

DBMS

specific

functionality

such

as

concatenation

date

manipulation

and

datatype

conversion

These

things

were

covered

in

the

DBMS

Specific

Functions

lab

This

approach

limited

some

of

what

could

be

done

in

the

first

labs

but

provided

a

solid

distinction

between

what

was

ANSI-standard

SQL

and

what

was

database-specific

Later

labs

used

these

database-specific

functions

in

various

lab

exercises

so

that

these

functions

which

are

crucial

in

the

real

world

were

mastered

In

addition

to

the

use

of

both

databases

in

labs

lecture

material

constantly

referred

to

how

various

design

administration

and

optimization

concepts

would

be

applied

in

both

databases

and

in

other

databases

such

as

MySQL

In

addition

exam

questions

asked

students

to

compare

the

capabilities

of

each

database

Other

assignments

led

students

into

an

exploration

of

the

pros

and

cons

of

various

database

engines

Two

of

these

were

for

students

to

write

short

papers

on

the

following

Research

one

of

the

following

databases

Sybase

Informix

MySQL

PostgreSQL

or

SQLWindows

Solo

Write

a

page

paper

comparing

it

to

Oracle

and

SQL

Server

Include

your

recommendations

regarding

the

circumstances

in

which

this

database

should

be

used

Research

what

people

on

the

Internet

say

comparing

SQL

Server

and

Oracle

Based

on

their

perspectives

and

your

own

experience

with

these

two

databases

write

pages

comparing

them

With

all

the

course

objectives

listed

above

the

Database

Fundamentals

course

makes

for

a

full

semester

Using

two

databases

instead

of

one

definitely

adds

to

the

challenge

of

fitting

in

all

the

material

This

first

attempt

led

to

the

realization

of

several

kinks

that

would

need

to

be

worked

out

before

it

could

be

attempted

again

These

are

listed

below

In

most

cases

any

given

SQL

lecture

had

barely

enough

time

for

covering

the

SQL

concepts

and

implementation

in

both

databases

forcing

the

instructor

to

scrimp

on

in-class

examples

This

meant

that

students

had

a

more

shaky

foundation

going

into

the

lab

assignment

However

it

should

be

noted

that

students

did

about

as

well

on

lab

assignments

as

prior

classes

One

possible

solution

would

be

to

focus

each

SQL

lecture

on

only

one

database

but

to

revisit

that

concept

in

the

following

lecture

when

the

other

database

is

used

In

past

semesters

using

only

one

database

it

was

possible

to

include

a

discussion

of

datatypes

along

with

the

DDL

lecture

Using

two

databases

the

DDL

lecture

had

to

be

expanded

which

did

not

leave

enough

time

for

thorough

coverage

of

datatypes

in

both

databases

A

solution

would

be

to

move

the

datatype

material

to

one

of

the

design

lectures

emphasizing

datatyping

as

a

design

step

By

switching

between

databases

database-specific

syntax

never

clicked

in

students

minds

They

seemed

less

able

than

prior

classes

to

apply

concatenation

and

datatype

conversions

without

looking

up

the

syntax

in

reference

material

While

this

is

regrettable

it

may

be

a

worthwhile

trade-off

for

gaining

an

understanding

of

what

is

ANSI-standard

vs.

database-specific

SQL

It

is

likely

that

when

students

move

to

the

real

world

and

settle

in

with

one

database

they

will

quickly

be

able

to

internalize

the

syntax

Some

students

expressed

that

they

would

have

preferred

going

through

all

labs

with

one

database

and

then

looking

at

differences

with

a

second

database

However

other

students

liked

working

with

both

databases

side

by

side

This

suggests

that

the

alternating

structure

may

need

to

be

tweaked

But

whatever

one

does

it

will

probably

not

work

with

every

learning

style

After

using

both

databases

almost

all

students

when

given

a

choice

used

SQL

Server

This

was

solely

because

of

a

perceived

superiority

in

the

user

interface

of

Query

Analyzer

versus

SQL

Plus

In

itself

this

is

not

a

bad

thing

because

one

of

the

goals

was

to

understand

the

differences

in

the

two

databases

But

in

future

semesters

the

instructor

may

want

to

force

a

choice

more

often

to

insure

that

students

will

be

exposed

more

equally

to

both

These

kinks

aside

both

instructor

and

students

considered

the

experiment

a

success

Their

papers

indicated

a

mature

appreciation

of

the

differences

between

Oracle

and

SQL

Server

and

how

they

compared

to

other

databases

Their

comments

in

class

indicated

that

they

understood

what

was

ANSI-standard

SQL

and

what

was

not

In

the

SQL

lab

exam

these

students

performed

as

well

as

previous

classes

of

students

indicating

that

the

cross-engine

approach

did

not

hinder

their

learning

Students

also

indicated

enthusiasm

for

being

able

to

list

experience

in

both

databases

on

their

resume

Compared

to

prior

semesters

students

left

the

course

more

comfortable

and

able

to

use

either

of

these

major

database

engines

to

accomplish

the

goals

of

a

given

information

system

-LSB-

-RSB-

Gorman

Michael

M.

-LRB-

-RRB-

Is

SQL

a

real

standard

anymore

The

Data

Administration

Newsletter

-LRB-

TDAN.com

-RRB-

-LRB-

Apr.

-RRB-

-LSB-

-RSB-

Gulutzan

Peter

-LRB-

-RRB-

Standard

SQL

-LRB-

Apr.

-RRB-

-LSB-

-RSB-

Wong

Wylie

-LRB-

-RRB-

IBM

passes

Oracle

in

database

market

html

-LRB-

June

-RRB-

O O O O O O O B O B I O O O O B O O O O B O O O O B O B O O O B O B I O O O O O O O O O O O O B O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O B O O O O O O B O O O O O B O O O O O O O O B O O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O B O B I O O O O O O O B O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O B O O O B O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O B O B I O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O B O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O B I O O O B O B I O O O O B O B I O O B O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O B O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O

dual environment
oracle
database
sql server
sql

sql language
course design
practical results
ansi-standard sql
teaching in it
educational fundamentals
database systems
student feedbacks
training vs. education