Machine Learning for 21 Century Biology and Medicine · Machine Learning for 21st Century Biology and Medicine Robert F. Murphy ... • The advent of machines capable of determining

Machine Learning for 21st Century Biology and Medicine

Robert F. Murphy Lane 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

cytoplasm

nucleus

cytoplasm

phospho- β-catenin

nucleus

cytoplasm

Nakopoulou, Mod Path. 2006

Chung, 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]

$0

$500

$1,000

$1,500

$2,000

$2,500

‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17 ‘18 ‘19 ‘20

Year Est Tota

l M

ark

et (m

illio

ns)

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

INPUT protein

channel

DNA

channel

Unmixing and Thresholding

Feature Extraction

preprocessing

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

Accura

cy

(%)

Finding location biomarkers • Compare

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

Pro

tein

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 types Cell 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!


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


We need to learn the entire matrix!


We cannot afford to exhaustively perform every experiment


X

X

X

X

X

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



We cannot afford to exhaustively perform every experiment Solution: 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)


Learning with internal and external data

X

X

X


1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

X

X

X


1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

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

X

X

X


2,7

,,…

1,7

,,…

X

X

X


2,7

,,…

1,7

,,…

1.9, 2.9, 365.4,…

2.1, -34.9, 5.4,…

X

X


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 Learning Optimized -QSAR Randomized Search

Active Learning Optimized -QSAR Randomized Search

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


Learning with internal data only


Group compounds that show similar effects


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

DrBox

Clinical Collaborators

Academic Software Partners

Funding Sources

Informatics Resources

Commercial Software Partners

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 expression Association 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

Epistasis ACGTTTTACTGTACAATT

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

Documents

Machine Learning for 21 Century Biology and Medicine · Machine Learning for 21st Century Biology and Medicine Robert F. Murphy ... • The advent of machines capable of determining