High-throughput measurement technologies have provided the field of biology with a wealth of molecular and genomic data. Machine learning approaches offer the opportunity to improve our understanding of biology and to directly impact on the quality of life, for example through disease prognosis and personalized drug therapy. In this talk, I will take a critical look at machine learning in biology. How does it differ from machine learning in other domains? What are the specific challenges and how are they addressed? I will describe our work based on The Cancer Genome Atlas (TCGA) and the Genomics of Drug Sensitivity in Cancer (GDSC) projects. Further, I will highlight the importance of interpretable models in biology. Particularly, only if computational models are intuitively understandable can experts overlay their domain knowledge and gain insight that allows them to suggest novel hypotheses and experiments. I will describe approaches that aim to bridge the gap between computational and biological complexity on the one hand and human interpretation on the other.

Machine learning in biology and why it doesn't make sense

Theo Knijnenburg Seattle DAML July meetup

Scientific background

1998-2003 MSc Electrical Engineering

2004-2008 PhD Computational Biology

2009-2010 Postdoc ISB

2011-2012 Postdoc Netherlands Cancer Institute

2013… (Senior) Research Scientist ISB

The Institute for Systems Biology

WE ARE NOT AN ACADEMIC

INSTITUTION

However we do have a deep

and integrated interest in

education both within and

outside of our institution.

WE ARE NOT A BIOTECH

COMPANY

However we do value

impacting society and making

a difference through our

research.

WE ARE A NON-PROFIT SCIENTIFIC

RESEARCH ORGANIZATION

We exist to make profound

breakthroughs in human health and

the environment, leveraging the

revolutionary potential of systems

biology.

The Institute for Systems Biology

An integrative approach towards biology

• Study a biological system as a whole, not just the individual components

• Acknowledge that information exists a multiple scales – Time, space, function

• Bring people from different disciplines together

The Institute for Systems Biology

The Institute for Systems Biology

Overview

• Machine learning in high-throughput biology

• Interpretation of supervised machine learning models

• An application: Drug response in cancer modeled as a combination of mutations

Machine learning in high-throughput biology

High-throughput: The use measurement techniques that allow us to rapidly obtain genome-wide molecular and genomic data. For example: - DNA sequence (~3 billion base pairs) - Gene expression (~20,000 genes)

Machine learning in high-throughput biology

• n<<p

• Current challenges in data generation are reflected in the quality of the data

• Lack of formalized knowledge

• Time series data (not so much)

n<<p n: objects/samples/data points/cases

p: observations/predictors/features/attributes

• n is small, because life is expensive • p is big, because life is complex • Consequences

– Proper test or validation sets are often not used – Feature selection/reduction are necessary – Statistical assumptions are often violated – Many specific techniques or modifications have

been proposed

n<<p n: ~100s

p: ~10,000s

Clin

ical

inf

orm

atio

n

tum

or c

hara

cter

istic

s

mic

roR

NA

exp

ress

ion

gene

exp

ress

ion

(mR

NA

)

DN

A m

ethy

latio

n

DN

A m

utat

ions

and

cop

y-nu

mbe

r abe

rrat

ions

BRCA GBM KIRC LUAD LUSC OV etc…

Current challenges in data generation are reflected in the quality of the data

• Technology side – Immature measurement technologies – Batch (or lab) effects

• Biology side – Context specificity of measurements – Many known/unknown covariates affect the data

• Human side – People that do study design are often unaware of power

calculations or statistics in general – Many manual steps involved – Difficulty in digitalizing information and metadata

Lack of formalized knowledge

• We are not really sure what we are measuring as we did not design the system

• Available knowledge is not formalized

Time series data (not so much)

• Measurements are expensive and often not automated

• Exceptions: time lapse MRIs, ECGs

• High-throughput time series data are coming

Quantified self Muse – the brain sensing headband

The guy that bought the headband trying to meditate

Experienced meditator

Interpretation of supervised machine learning models

Application of supervised machine learning

1. Prediction

The value or class of a (new or unseen) case can be predicted.

2. Interpretation

The inferred models inform us about the relationship between the input and the output. They specify how the input variables are (mathematically) interacting with each other to produce the output variable.

A Card Puzzle

A D 4 7 If a card has a vowel on one side, then it has an even number on the other side. Which of the cards would one need to turn over to see if the claim is true or false?

A Card Puzzle

BEER SODA 25 17

If a person is drinking beer, then they are over 21 years of age. Which of the cards would one need to turn over to see if the claim is true or false?

Interpretation

– Variables should make intuitive sense

– Number of variables is small

– Mathematical relation between variables

should be easy to understand

Why interpretable models?

– Allows to add domain knowledge

• A limited amount of knowledge is and can be formalized

– Generate hypotheses and define experiments • Understandable models are actionable models

Machine learning in biology doesn’t make sense, because the inferred models often don’t make sense to the domain experts.

How to generate interpretable models?

– Visualization

• Raw, transformed, reduced, summarized data • Inferred models and associations between features or samples

– Learn complex models

• Postprocessing: interpretation step

– Learn simple small models • Directly interpretable

Drug response in cancer modeled as a combination of

mutations

Background - Cancer

Background - Cancer

Background - Cancer Treatment modes • Surgery

• Radiotherapy

• Chemotherapy

Background - Cancer

Bacterial cell Human cell Cancer cell

ANTI BIOTICS

ANTI CANCER DRUGS

Cell lines, mouse models and patients

Realism, Relevance

Throughput, Controlled environment

Genomics of Drug Sensitivity in Cancer (GDSC) cancer cell line panel

1047 cancer cell lines

Genomics of Drug Sensitivity in Cancer (GDSC) cancer cell line panel

1047 cancer cell lines

532 drugs

Drug response IC50s

Genomics of Drug Sensitivity in Cancer (GDSC) cancer cell line panel

1047 cancer cell lines

532 drugs

Drug response IC50s

Gene expression data

Copy number variation data

DNA methylation data

Cell line description

Mutation data from exome sequencing

Genomics of Drug Sensitivity in Cancer (GDSC) cancer cell line panel

1047 cancer cell lines

532 drugs

Drug response IC50s

Gene expression data

Copy number variation data

DNA methylation data

Cell line description

Mutation data from exome sequencing

Genomics of Drug Sensitivity in Cancer (GDSC) cancer cell line panel

1047 cancer cell lines

532 drugs

Drug response IC50s

Gene expression data

Copy number variation data

DNA methylation data

Cell line description

Mutation data from exome sequencing

How does the mutation landscape of the cancer cell affects the way in which

it responds to anti-cancer drugs?

Goal - Actionable models

• Models with a small number of variables that make intuitive sense

• Explain drug response as a logic function of mutations

| Sensitivity to AZ628

BRAF KRAS

BCR_ABL

“OR”

& Sensitivity to Metformin

RB1

~PTEN

“AND”

1000 cell lines

500 cancer genes IC50

Mytomycin C mutation

wild type Sensitive (36)

P53 CDKN2A BRCA2 | KMD6A → Sensitive

Approach – Logic model Logic Optimization for Binary Input to Continuous Output

-4 -2 0 2 4 6 8-10

0

10

20

30

40

50

60

70

IC50

Coun

tPLX4720

-4 -2 0 2 4 6 8-10

0

10

20

30

40

50

60

70

IC50

Coun

tPLX4720

Sensitive Resistant

Large error

Small error

BRAF inhibitor PLX4720

Log IC50 (uM)

Approach

1. Output small logic models that are understandable by domain experts (= actionable), while retaining continuous phenotype information • Generate hypotheses • Define experiments • Find biomarkers

2. Find the optimal solution fast by ILP formulation solved

using dedicated and advanced software (CPLEX)

3. Find solutions with predefined constraints on statistical performance criteria, which facilitates clinical application

Contributions

NKI Netherlands

Cancer Institute

TU Delft Delft University of

Technology

Sanger Cancer Genome

Project - Wellcome Trust Sanger Institute

EBI European

Bioinformatics Institute

ISB Institute for

Systems Biology

Lodewyk Wessels Bob Duin Graham Bignell Francesco Iorio Dick Kreisberg

Julian de Ruiter Jeroen de Ridder Howard Lightfoot Michael Menden Vesteinn Thorsson

Mathew Garnett Julio Saez-Rodriguez Sheila Reynolds

Ultan McDermott Brady Bernard

Ilya Shmulevich

Nitin Baliga

Gustavo Glusman

Acknowledgements

Btw: Machine learning in biology does make sense, because the challenges are fundamentally interesting and relevant.

tknijnenburg@systemsbiology.org