Machine Learning for 21st Century Biology and Medicine

Robert F. MurphyLane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering and Machine Learning

Outline• Digital Pathology and Location Biomarkers• Active Learning for Drug Development• Personal genome analysis: plans and

challenges• Challenges of automated systems for

biomedicine

DIGITAL PATHOLOGY AND LOCATION BIOMARKERS

Motivation

• Protein function is modulated by changing its subcellular abundance, activity or location

• Differential protein abundance is routinely studied for biomarker discovery

Motivation

• Protein function is modulated by changing its subcellular abundance, activity or location

• Differential protein abundance is routinely used for biomarker discovery

• Less work has been done on using subcellular location differences for biomarker discovery

Motivation• Protein function is modulated by affecting its

subcellular abundance or location • Differential protein abundance is routinely used for

biomarker discovery• Less work has been done on using subcellular

location differences for biomarker discovery• Ultimately, both abundance and location

biomarkers are important for the systematic study of disease processes as well as the development of clinical therapeutics

Example Location Biomarker• Cytoplasmic phospho-β-catenin inversely correlated with

tumor size and stage (breast, skin cancer) .

healthy

cancer

nucleus

nucleus

cytoplasmnucleusnucleus

cytoplasm

phospho- β-catenin

nucleusnucleus

cytoplasm

Nakopoulou, Mod Path. 2006Chung, Clinical Cancer Res., 2001

Digital Pathology• Dynamic, image-based

environment that enables the acquisition, management and interpretation of pathology information generated from a digitized glass slide. [Source: Digital Pathology Association] Year

Opportunity for Automated Analysis

• Increasing availability of tissue images in digital form opens opportunity for automating tasks performed by pathologists

Opportunity for New Analyses• Potentially more important opportunity for

performing analyses that are difficult for pathologists to perform visually

• Example: detecting location biomarkers

Human Protein Atlas: a compendium of protein subcellular staining patterns in IHC images

http://www.proteinatlas.org (Uhlén, Pontén et al)

HPA: Protein patterns visualized with immunohistochemistry

• Hematoxylin stains nuclei purple• Diaminobenzidine detects a mono-specific

antibody against a particular protein with brown product

Brightfield image, ~1 mm in width Brightfield image, ~0.1 mm in width

10um

Framework for Automated Determination of Subcellular Location

INPUTprotein channel

DNA channel

Unmixing and ThresholdingUnmixing and Thresholding Feature ExtractionFeature Extraction

preprocessing

Query Classifier to assign subcellular location based on

image features

Query Classifier to assign subcellular location based on

image features

QUERY IMAGE

[0.4,0.2, 1.6,2.8,….0.6].

This is a ER pattern

Cytoplasm Endoplasmic Reticulum Golgi Intermediate Filament Lysosome Membrane Microtubule Mitochondria Nucleus Peroxisome Secreted

The classifier is trained to distinguish 11 subcellular location classes

Automated analysis works!A

CT

UA

L LA

BE

L

PREDICTED LABEL

Accuracy

(%)

Finding location biomarkers• Compare

subcellular location for normal tissues with that for tumor derived from same tissue (for six tissues)

Protein

Tissue

Red=different

Some markers are tissue-specific, some more general

Next steps• Patent pending on biomarker detection• Clinical collaborations to verify that these

proteins are location biomarkers and to determine whether they have diagnostic/prognostic/theranostic value

• Incorporation of analysis technology into digital pathology platforms

ACTIVE LEARNING FOR DRUG DEVELOPMENT

Molecular Complexity of Organisms~104-105 proteins

~102 cell typesCell Biology: We want to know where all the proteins are in all the cell types

Molecular Complexity of Perturbagens~1060 potential small, soluble molecules

~1012 potential RNA inhibitors

Drug development: We want to know how a subset of proteins and cell types

are affected by these perturbagens

Establish Assay for a Target

Traditional HCS/HTS

Identify Positive and Negative Controls

Negative Positive Measured Unmeasured

Screen 107 Compounds

Negative Positive

Two problems:(1) We don’t know effects on other targets

Negative Positive Intermediate

Two problems:(1) We don’t know effects on other targets

Comprehensive screening for one target does not reveal side effects!

Negative Positive Intermediate

Two problems:(1) We don’t know effects on other targets(2) We have learned nothing for the next target

Negative Positive Intermediate

We need to learn the entire matrix!

Negative Positive Intermediate

We cannot afford to exhaustively perform every experiment

Negative Positive Intermediate

X

X

X

X

X

We cannot afford to exhaustively perform every experimentSolution: just do some …predict the rest

Negative Positive Intermediate

Negative Positive Intermediate

We cannot afford to exhaustively perform every experimentSolution: just do some and predict the rest

Two versions of problem• When information is available not only about

the readout from experiments but about the similarity of compounds to each other and targets to each other (“internal and external data”)

• When information is only available about readout of experiments (“internal only”)

Two versions of readout• Scalar (e.g., percentage of maximum hit)• Vector (e.g., percentage of probe in different

compartments)

Negative Positive Intermediate

Learning with internal and external data

X

X

X

Negative Positive Intermediate

1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

X

X

X

Negative Positive Intermediate

1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

NB: Similar to QSAR (Hansch et. al. 1972)

X

X

X

Negative Positive Intermediate

2,7,

,… 1,7,

,…

X

X

X

Negative Positive Intermediate

2,7,

,… 1,7,

,…

1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

X

X

Negative Positive Intermediate

2,7,

,… 1,7,

,…

1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

PubChem Data Preparation• Assays: 177

– 108 in vitro– 69 in vivo– Sign of score reflects type of assay (inhibition or activation)

• Unique Protein Targets: 133• Compounds: 20,000• Experiments: ~1,000,000 (30% coverage)

• Goal: discover hits - drug-target pairs whose |rank score| > 80• Very few hits (0.096%)

38

Active LearningOptimized -QSARRandomized Search

Active LearningOptimized -QSARRandomized Search

With only 2.5% of the matrix covered, we can identify 57% of the active compounds!

Negative Positive Intermediate

Learning with internal data only

Negative Positive Intermediate

Group compounds that show similar effects

Negative Positive Intermediate

Group targets that show similar behavior

X

Predict unmeasured experiments where possible

Choose a batch of experiments balanced between those for which prediction is available and not

Continue until you can predict everything and predictions are accurate

Next steps• Patent pending for active learning methodology• Collaborate with pharmaceutical companies to

demonstrate that methodology would have worked using complete datasets

• Use with robotics to tackle new problems • Extend to combinations of perturbagens• Many problems in biology of similar complexity

PERSONAL GENOME ANALYSIS: PLANS AND CHALLENGES

Personal genome sequencing arrives• The advent of machines capable of

determining personal genome sequences for $1,000 will user in a new era of personalized medicine

• Danger in possible proliferation of fragmented, proprietary genome analysis software

Initial funding from Ion Torrent

Operating Principles• Open source, free licensing (GPLv2)• Encourage collaborations/contributions under

that licensing• Frequent releases• Enable compression, assuming resequencing

easy• Never-ending learning (online learning)

1. Clinical Collaborations• Clinical collaborators provide personal genome

sequence, other omics data (optional) and clinical phenotype information (disease, onset, severity, survival time, treatment responsiveness)

• Primary goals: Development and testing of methods AND learning of new associations

• Data typically confidential/proprietary at least until publication

• Typically requires research agreement

2. Software Collaborations• Open source, no license fee software

model

• New releases frequently

• Collaborators and contributors welcome

• No agreements necessary

3. Dissemination Collaborations• Commercial organizations who provide

data storage and computing resources (e.g., cloud)

• Entire analysis pipeline is open source (including DrBox)

• Personnel may make contributions to software development and testing

raw sequence reads

genome features

disease and treatment history

genome feature:disease associations

gene:pathway associations

probabilistic graphical model

pathway:disease associations

protein:disease associations

annotated reference genome

model can predict probability of any missing item

probability estimates continually updated as new data added

Overview

clinical tests

predicted disease susceptibility

identified disease features

Intermediate Phenotype

Genetic Basis of Complex Diseases

Healthy

Cancer

ACTCGTACGTAGACCTAGCATTACGCAATAATGCGA

ACTCGAACCTAGACCTAGCATTACGCAATAATGCGA

TCTCGTACGTAGACGTAGCATTACGCAATTATCCGA

ACTCGAACCTAGACCTAGCATTACGCAATTATCCGA

ACTCGTACGTAGACGTAGCATAACGCAATAATGCGA

TCTCGTACCTAGACGTAGCATAACGCAATAATCCGA

ACTCGAACCTAGACCTAGCATAACGCAATTATCCGA

Causal SNPs

Clinical records

Gene expressionAssociation to intermediate phenotypes

Structured Association

Phenome Structure

Graph-guided fused lasso (Kim & Xing, PLoS Genetics, 2009)

Graph

Tree-guided fused lasso (Kim & Xing, ICML 2010)

Tree

Temporally smoothed lasso (Kim, Howrylak, Xing, Submitted)

Dynamic Trait

Genome Structure

Stochastic block regression (Kim & Xing, UAI, 2008)

Linkage Disequilibrium

Multi-population group lasso (Puniyani, Kim, Xing, ISMB 2010)

Population Structure

EpistasisACGTTTTACTGTACAATT

Group lasso with networks(Lee, Jun, Xing, NIPS 2010)

Structured Association: a New Paradigm

• Significant tests for each loci

• Multivariate linear regression

• Lasso (“sparse” linear regression)

CHALLENGES OF AUTOMATED SYSTEMS FOR BIOMEDICINE

Challenges to society• For all automated systems, as with human systems, errors are

inevitable. For current systems, the consequences of machine error are easily dealt with, whether it is by retrieving misdirected mail, ignoring an uninteresting recommendation, or averaging in some unsuccessful trades with many successful ones. However, as automated decision-making is extended to biomedicine, the consequences of error may be more difficult to address.

• Furthermore, a new question arises: how will people, especially scientists and physicians, react to the existence of systems that understand their fields better than they do?

• Past and Present Students and Postdocs– Justin Newberg (Baylor), Estelle Glory, Arvind Rao (M.D.

Anderson), Armaghan Naik, Josh Kangas

• Funding– NSF, NIH, Commonwealth of Pennsylvania,

Ion Torrent

• Collaborators/Consultants– Mathias Uhlen, Emma Lundberg, Tom Mitchell, Chris Langmead,

Lans Taylor, Jonathan Rothberg, Ziv Bar-Joseph, Seyoung Kim, Kathryn Roeder, Russell Schwartz, Eric Xing

Acknowledgments