Introduction to translational and clinical bioinformatics Connecting complex molecular information...

Introduction to translational and clinical bioinformatics

Connecting complex molecular information to clinically relevant decisions

using molecular profiles

Constantin F. Aliferis M.D., Ph.D., FACMI

Director, NYU Center for Health Informatics and Bioinformatics

Informatics Director, NYU Clinical and Translational Science Institute

Director, Molecular Signatures Laboratory,

Associate Professor, Department of Pathology,

Adjunct Associate Professor in Biostatistics and Biomedical Informatics, Vanderbilt University

1

Alexander Statnikov Ph.D.

Director, Computational Causal Discovery laboratory

Assistant Professor, NYU Center for Health Informatics and Bioinformatics, General Internal Medicine

Goals• Understand spectrum of Bioinformatics and Medical informatics activities • Understand basic concepts of clinical/translational Bioinformatics• Understand basic concepts of molecular profiling• Introduction to high-throughput assays enabling molecular profiling• Introduction to computational data analytics/bioinformatics enabling molecular

profiling• Understand analytic challenges and pitfalls/interpretation issues• Discuss case study of profiles used to diagnose/treat patients• Perform hands-on development of a molecular profile, finding novel biomarkers and

testing profile/markers accuracy

Discussion supported by general literature and heavily grounded on:- NYUMC informatics experts/research projects/grants/papers/entities/software

systems- Commercially availiable modalities & assays

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Overview

• Session #1: Basic Concepts • Session #2: High-throughput assay

technologies • Session #3: Computational data analytics • Session #4: Case study / practical applications • Session #5: Hands-on computer lab exercise

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Session #1: Basic Concepts

• Understand spectrum of Bioinformatics and Medical informatics activities - NYUMC informatics

• Understand basic concepts of clinical/translational Bioinformatics

• Understand basic concepts of molecular profiling• ALSO:

- emails/names/interests- adjustments to plan

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NYU Center for health Informatics & Bioinformatics: Broad Plan

Health Informatics Bioinformatics

BPIC (best Practices Integrative

Consultation Core/Service

Literature Synthesis & Benchmarking studies

Design and execution of studies

Method-problem “matchmaking”

Microarray Informatics:

i.Upstreamii.Differential expression, iii.Pathway inferenceiv.Molecular profiles

Next-gen sequencing informatics :

Upstream analysesi. Chi-seqii. RNA seqiii. Epigeneticsiv. Microbiomicsv. micro RNA studiesvi. CNV & splice variation studiesvii. Digital RNAviii. Denovo sequencing & re-sequencingDownstream analyses

Educational informatics

Evidence based medicine, and

Information retrieval Informatics

Library Collaborative

Data Integration & Mining:

-Data warehouse & interfacing with EMR- Omics LIMS- Genomic EMR- Biospecimen management-research protocol database systems and management team-Data mining service-Data Mining software

Infrastructure & Integrative Methods/Activities

High Performance Computing Facility

MS/PhD (& Post-doc Fellowship) Program

Continuing Education• Workshops & tutorials•Paper digest•Research Colloquium•Invited Speakers

Research labs• Kluger

•Molecular Signatures•EBM, IR & Scientometrics

•Computational Causal Discovery

CTSI

Integrate/Focus Existing Informatics and Increase

Collaborations5

ProteomicsInformatics

CancerCenter

Genetics-Genomics

COEs

Multi-modal & Integrative

studies

Current Capabilities: Areas

Health Informatics

Educational informatics

Evidence based medicine, and

Information retrieval Informatics

Library Collaborative

Infrastructure & Integrative Methods/Activities

High Performance Computing Facility

MS/PhD (& Post-doc Fellowship) Program

Continuing Education• Workshops & tutorials•Paper digest•Research Colloquium•Invited Speakers

Research labs• Kluger•Molecular Signatures•EBM, IR & Scientometrics•Computational Causal Discovery

CTSI

Integrate/Focus Existing Informatics and Increase

Collaborations6

Data Integration & Mining:

-Data warehouse & interfacing with EMR- Omics LIMS- Genomic EMR- Biospecimen management-research protocol database systems and management team-Data mining service-Data Mining software

Bioinformatics

BPIC (best Practices Integrative

Consultation Core/Service

Literature Synthesis & Benchmarking studies

Design and execution of studies

Method-problem “matchmaking”

Microarray Informatics:

i.Upstreamii.Differential expression, iii.Pathway inferenceiv.Molecular profiles

Next-gen sequencing informatics :

Upstream analysesi. Chi-seqii. RNA seqiii. Epigeneticsiv. Microbiomicsv. micro RNA studiesvi. CNV & splice variation studiesvii. Digital RNAviii. Denovo sequencing & re-sequencingDownstream analysesProteomics

Informatics

CancerCenter

Genetics-Genomics

COEs

Multi-modal & Integrative

studies

Current & Future capabilities Health Informatics

Educational informatics

Evidence based medicine, and Information

retrieval Informatics

Library Collaborative

Content management, medical simulations

•Filter Medline according to content and quality• Filter Web for health advice quality• Predict future citations of articles• Classify individual citations as instrumental or not• Identify special types of articles• Construct citation histories & Analyze impact of articles• Integrate and manage queries and related content• Combine and optimize knowledge source searches• New “find a researcher”•“Find a collaborator”

Apply, evaluate, refine next-gen IR methods

-Data warehouse needs; software acquisition; implementation- OMICS LIMS needs capture; vendor product assessment; funds; sofwtare purchase and implementation; integration with billing and EMR-Biospecimen management-Research protocol database system (eVelos)-Data base management team-Data mining service-Data mining engine: faculty; funds; prototype; implementation; evaluation

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Data Integration & Mining:

-Data warehouse & interfacing with EMR- Omics LIMS- Genomic EMR- Biospecimen management-research protocol database systems and management team-Data mining service-Data Mining software

Current & Future capabilities Infrastructure & Integrative Methods/Activities

High Performance Computing Facility

MS/PhD (& Post-doc Fellowship) Program

Continuing Education• Workshops & tutorials•Paper digest•Research Colloquium•Invited Speakers

Research labs• Kluger

•Molecular Signatures•EBM, IR & Scientometrics

•Computational Causal Discovery

CTSI

Integrate/Focus Existing Informatics and Increase

Collaborations

(supported by rest of objectives)

Sequencing server; hectar1; hectar2;

Funds; needs; grants; personnel post; specs; room/networking/access; Personnel hires; hw install; licenses; BP; launch

• Kluger TF /Regulation studies; high-throughput outcome prediction, specialized clustering methods• Molecular Signatures development of molecular signatures for diagnosis outcome prediction and personalized medicine, discovery of diagnostic/imaging biomarkers and putative drug targets , deployment of signatures, automated software, new methods•EBM, IR & Scientometrics development and evaluation of next-gen IR and scientometric models and studies • Computational Causal Discovery discovery of pathways; studies of causal validity of bioinformatics discovery methods, multiplicity studies, automated software, active learning/experiment number minimization

Formal Training in Biomedical Informatics at pre and post-doctoral levels

Continuing Education• Workshops & tutorials• Paper digest• Research Colloquium• Invited Speakers

Faculty and Staff career development; Informatics Affiliates; Working Collaborations with Courant, Polytechnic, NYC Informatics and other non-NYUMC

entities8

Current & Future capabilities Bioinformatics

BPIC (best Practices Integrative

Consultation Core/Service

•Literature Synthesis & Benchmarking studies•Method-problem “matchmaking”•Design and execution of studies• Study publication assistance

Microarray Informatics: Experiment design, assay execution, differential expression, pathway mapping, pathway-specific testing (GSEA/GSA), de novo pathway discovery, phylogeny, clustering, hybrid experimental/observational designs; SNP arrays; ChIP-on-ChIP analyses, aCGH, tiled arrays, etc…

Sequencing Informatics: Chip-Seq analysis, digital gene expression, de novo sequence assembly & reassembly, CNV analysis, epigenomic studies, microbiomics

Proteomics Informatics: platform-specific pre-processing, differential abundance, peptide-protein mapping, protein identification, de novo protein interaction network inference, protein modification and structure studies,…

Cancer Center

Area-specific (Disease, Assay)

Informatics

Genetics-Genomics

COEs

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Multi-modal Integrative and Higher-level Informatics:• Molecular Signatures & linking high-dimensional data to phenotype development of molecular signatures for diagnosis, outcome prediction and personalized medicine; in silico signature scanning, in silico signature equivalence, discovery of diagnostic/imaging biomarkers and putative drug targets , deployment of signatures, automated software, novel methods• Mechanistic /causative studies discovery of pathways; multiplicity studies,

TS/DBN designs, automated software, active learning/experiment number minimization

• Integrating clinical lab, text, imaging and high throughput data in CTs/prospective studies or exploratory retrospective ones

Summary Contacts (Until Centralized Consultation Service is Launched)

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Molecular Signatures

Definition =

computational or mathematical models that link high-dimensional molecular information to phenotype of interest

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Molecular Signatures

Gene markers

New drug targets

Molecular Signatures: Main Uses

1. Direct benefits: Models of disease phenotype/clinical outcome & estimation of the model performance

• Diagnosis• Prognosis, long-term disease management• Personalized treatment (drug selection, titration) (“predictive” models)

2. Ancillary benefits 1: Biomarkers for diagnosis, or outcome prediction• Make the above tasks resource efficient, and easy to use in clinical practice• Helps next-generation molecular imaging• Leads for potential new drug candidates

3. Ancillary benefits 2: Discovery of structure & mechanisms (regulatory/interaction networks, pathways, sub-types)

• Leads for potential new drug candidates

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Molecular SignaturesThe FDA calls them “in vitro diagnostic multivariate index assays”

1. “Class II Special Controls Guidance Document: Gene Expression Profiling Test System for Breast Cancer Prognosis”:

- addresses device classification2. “The Critical Path to New Medical Products”:- identifies pharmacogenomics as crucial to advancing medical product development and personalized

medicine. 3. “Draft Guidance on Pharmacogenetic Tests and Genetic Tests for Heritable Markers” & “Guidance for Industry: Pharmacogenomic Data Submissions”- identifies 3 main goals (dose, ADEs, responders),- define IVDMIA,- encourages “fault-free” sharing of pharmacogenomic data,- separates “probable” from “valid” biomarkers,- focuses on genomics (and not other omics),

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• Increased Clinical Trial sample efficiency, and decreased costs or

both, using placebo responder signatures ;• In silico signature-based candidate drug screening;• Drug “resurrection”• Establishing existence of biological signal in very small sample

situations where univariate signals are too weak; • Assess importance of markers and of mechanisms involving those• Choosing the right animal model• …?

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Less Conventional Uses of Molecular Signatures

OvaSure

Agendia Clarient Prediction Sciences

Veridex

LabCorp

University Genomics Genomic Health

BioTheranostics Applied Genomics Power3

Correlogic Systems

Recent molecular mignatures available for patient care

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Company Product Disease Purpose

Agendia MammaPrint Breast cancerRisk assessment for the recurrence of distant metastasis in a breast cancer patient.

Agendia TargetPrint Breast cancerQuantitative determination of the expression level of estrogen receptor, progesteron receptor and HER2 genes. This product is supplemental to MammaPrint.

Agendia CupPrint Cancer Determination of the origin of the primary tumor.

University Genomics

Breast Bioclassifier Breast cancerClassification of ER-positive and ER-negative breast cancers into expression-based subtypes that more accurately predict patient outcome.

ClarientInsight Dx Breast Cancer

ProfileBreast cancer Prediction of disease recurrence risk.

ClarientProstate Gene Expression

ProfileProstate cancer

Diagnosis of grade 3 or higher prostate cancer.

Prediction Sciences

RapidResponse c-Fn Test StrokeIdentification of the patients that are safe to receive tPA and those at high risk for HT, to help guide the physician’s treatment decision.

Genomic Health OncotypeDx Breast cancerIndividualized prediction of chemotherapy benefit and 10-year distant recurrence to inform adjuvant treatment decisions in certain women with early-stage breast cancer.

bioTheranostics CancerTYPE ID Cancer Classification of 39 types of cancer.

bioTheranostics Breast Cancer Index Breast cancerRisk assessment and identification of patients likely to benefit from endocrine therapy, and whose tumors are likely to be sensitive or resistant to chemotherapy.

Applied Genomics

MammaStrat Breast cander Risk assessment of cancer recurrence.

Applied Genomics

PulmoType Lung cancerClassification of non-small cell lung cancer into adenocarcinoma versus squamous cell carcinoma subtypes.

Applied Genomics

PulmoStrat Lung cancerAssessment of an individual's risk of lung cancer recurrence following surgery for helping with adjuvant therapy decisions.

Correlogic OvaCheckOvarian cancer

Early detection of epithelial ovarian cancer.

LabCorp OvaSureOvarian cancer

Assessment of the presence of early stage ovarian cancer in high-risk women.

Veridex GeneSearch BLN Assay Breast cancer Determination of whether breast cancer has spread to the lymph nodes.

Power3 BC-SeraPro Breast cancer Differentiation between breast cancer patients and control subjects.

Molecular signatures in the market (examples)

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MammaPrint

• Developed by Agendia (www.agendia.com)• 70-gene signature to stratify women with

breast cancer that hasn’t spread into “low risk” and “high risk” for recurrence of the disease

• Independently validated in >1,000 patients• So far performed 12,000 tests• Cost of the test is $3,200• In February, 2007 the FDA cleared the

MammaPrint test for marketing in the U.S. for node negative women under 61 years of age with tumors of less than 5 cm.

• TIME Magazine’s 2007 “medical invention of the year”.

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CupPrint

• Developed by Agendia (www.agendia.com)• ~500-gene (~1900 probes) signature to identify

primary site of 49 different types of carcinomas as well as other types of cancer such as sarcoma and melanoma.

• Several independent validation studies

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ColoPrint

• In development & validation by Agendia (www.agendia.com)• Multi-gene expression signature to determine the risk for

recurrence in colorectal cancer patients• Planning to seek FDA approval

References:• http://cancergenetics.wordpress.com/category/coloprint/• http://www.bioarraynews.com/issues/7_34/features/141935-1.html• http://life-science-ventures.com/downloads/PressreleaseColoPrintfinalJuly10th2007.pdf

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Oncotype DXDevelopment synopsis

Main reference: Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351(27):2817-26.

• Developed by Genomic Health (www.genomichealth.com )• 21-gene signature to predict whether a woman with localized, ER+ breast cancer

is at risk of relapse• Independently validated in >1,000 patients• So far performed 55,000 tests• Cost of the test is $3,650• Reimbursement. Information about reimbursement for molecular signatures from

Aetna: http://www.aetna.com/cpb/medical/data/300_399/0352.html • Oncotype DX did not undergo FDA review. Here is an article that mentions FDA

review of Oncotype DX (slightly outdated): http://www.sciencemag.org/cgi/content/full/303/5665/1754

• The following paper shows the health benefits and cost-effectiveness benefits of using Oncotype DX: http://www3.interscience.wiley.com/cgi-bin/abstract/114124513/ABSTRACT

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CancerType ID

• Developed by AviaraDX (www.aviaradx.com)• 92-gene signature to classify 39 tumor types• Signature developed by GA/KNN• “Compressed version” of CupPrint

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Breast Cancer Index

• Developed by AviaraDX (www.aviaradx.com)• Uses 7 genes (combines 5-gene MGI signature and 2-gene H/I

signature)• Stratifies breast cancer patients into groups with low or high

risk of cancer recurrence and good or poor response to endocrine therapy.

• Validated in thousands of patients (treated & untreated)

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GeneSearch Breast Lymph Node (BLN) Assay

• Developed by Veridex (www.veridex.com), a Johnson & Johnson company• Test to detect if breast cancer has spread to the lymph nodes• The GeneSearch BLN uses real-time reverse transcriptase-polymerase chain

reaction (RT-PCR) to detect ammoglobin (MG) and cytokeratin 19 (CK 19) in lymph nodes.

• FDA approved• Featured in TIME’s 2007 Top 10 Medical Breakthroughs list

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MammoStrat

• Developed by Applied Genomics (http://www.applied-genomics.com)

• The test is based on 5 biomarkers.• The test is used to classify individual

patients as having an AGI-defined high-, moderate-, or low-risk of breast cancer recurrence following surgical removal of their primary tumor and treatment with tamoxifen alone.

• Independently validated in >1000 patients

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NuroPro

• Developed by Power3 (http://www.power3medical.com/)

• Early detection of neurodegenerative diseases: Alzheimer’s disease, ALS (Lou Gehrig’s disease), and Parkinson’s disease.

• Validation study in progress.• Based on 59 proteins.

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BC-SeraPro

• Developed by Power3 (http://www.power3medical.com/)

• Test for diagnosis of breast cancer (breast cancer case vs. control).

• Validation study in progress.• Based on 22 proteins.• Uses linear discriminant analysis;

outputs a probability score.

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Key ingredients for developing a molecular signature

Well-defined clinical problem &access to patients

High-throughput assays

Computational &Biostatistical

Analysis

Molecular Signature

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Challenges in Computational Analysis of omics data for development of molecular signatures

• Relatively easy to develop a predictive model + even easier to believe that a model is good when it is not false sense of security

• Several problems exist: some theoretical and some practical• Omics data has many special characteristics and is tricky to

analyze!

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OvaCheck

• Developed by Correlogic (www.correlogic.com)• Blood test for the early detection of epithelial ovarian cancer • Failed to obtain FDA approval • Looks for subtle changes in patterns among the tens of thousands of

proteins, protein fragments and metabolites in the blood• Signature developed by genetic algorithm• Significant artifacts in data collection & analysis questioned validity of the

signature:- Results are not reproducible- Data collected differently for different groups of patientshttp://www.nature.com/nature/journal/v429/n6991/full/429496a.html

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Data Set 1 (Top), Data Set 2 (Bottom)

Cancer

Normal

Other

Clock Tick4000 8000 12000

Cancer

Normal

Other

A

B

C

D

E

F

Figure from Baggerly et al (Bioinformatics, 2004)

Problem with OvaCheck

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Molecular Signatures

Gene markers

New drug targets

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Brief History of main “omics” technology: gene expression microarrays

• 1988: Edwin Southern files UK patent applications for in situ synthesized, oligo-nucleotide microarrays

• 1991: Stephen Fodor and colleagues publish photolithographic array fabrication method

• 1992: Undeterred by NIH naysayers, Patrick Brown develops spotted arrays

• 1993: Affymax begets Affymetrix

• 1995: Mark Schena publishes first use of microarrays for gene expression analysis• Edwin Southern founds Oxford Gene Technologies

• 1996: First human gene expression microarray study published• Affymetrix releases its first catalog GeneChip microarray, for HIV, in April

• 1997: Stanford researchers publish the first whole-genome microarray study, of yeast

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Brief History of main “omics” technology: gene expression microarrays

(The scientist 2005)

• 1998: Brown's lab develops CLUSTER, a statistical tool for microarray data analysis; red and green "thermal plots" start popping up everywhere

• 1999: Todd Golub and colleagues use microarrays to classify cancers, sparking widespread interest in clinical applications

• 2000: Affymetrix spins off Perlegen, to sequence multiple human genomes and identify genetic variation using arrays

• 2001: The Microarray Gene Expression Data Society develops MIAME standard for the collection and reporting of microarray data

• 2003: Joseph DeRisi uses a microarray to identify the SARS virus• Affymetrix, Applied Biosystems, and Agilent Technologies individually array human

genome on a single chip

• 2004: Roche releases Amplichip CYP450, the first FDA-approved microarray for diagnostic purposes

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An early kind of analysis: learning disease sub-types by clustering

patient profiles

Rb

p53

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Clustering: seeking ‘natural’ groupings & hoping that they will be useful…

Rb

p53

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E.g., for treatment

Rb

p53

Respond to treatment Tx1

Do notRespond to treatment Tx1

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E.g., for diagnosis

Rb

p53

Adenocarcinoma

Squamous carcinoma

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Another use of clustering

• Cluster genes (instead of patients):– Genes that cluster together may belong to the

same pathways– Genes that cluster apart may be unrelated

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Unfortunately clustering is a non-specific method and falls into the ‘one-solution fits all’ trap when used for

prediction

Rb

p53

Respond to treatment Tx2

Do notRespond to treatment Tx2

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Clustering is also non-specific when used to discover pathway membership, regulatory control, or other

causation-oriented relationships

G1

Ph

G2

G3

It is entirely possible in this simple illustrative counter-example for G3 (a causally unrelated gene to the phenotype) to be more strongly associated and thus cluster with the phenotype (or its surrogate genes) more strongly than the true oncogenic genes G1, G2

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Two improved classes of methods

• Supervised learning predictive signatures and markers

• Regulatory network reverse engineering pathways

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Supervised learning : use the known phenotypes (a.k.a “labels) in training data to build signatures or

find markers highly specific for that phenotype

TRAININSTANCES

APPLICATIONINSTANCES

A

B C

D E

A1, B1, C1, D1, E1

A2, B2, C2, D2, E2

An, Bn, Cn, Dn, En

INDUCTIVE ALGORITHM Classifier

ORRegression Model

CLASSIFICATION PERFORMANCE

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TRAININSTANCES

A

B C

D E

A1, B1, C1, D1, E1

A2, B2, C2, D2, E2

An, Bn, Cn, Dn, En

PERFORMANCE

A

B C

D

E

INDUCTIVE ALGORITHM

Regulatory network reverse engineering

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Supervised learning: a geometrical interpretation

+

+

+

+

++

+

+ +

+

p53

Rb

??

P1

P4

P2

P3

P5

Cancer patients

Normals

New case, classified as normal

New case, classified as cancer

SVM classifier

+

+

+

+

++

+

+ +

+

p53

Rb

??

P1

P4

P2

P3

P5

Cancer patients

Normals

New case, classified as normal

New case, classified as cancer

SVM classifier

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• 10,000-50,000 (regular gene expression microarrays, aCGH, and early SNP arrays)

• >500,000 (tiled microarrays, new SNP arrays)• 10,000-300,000 (regular MS proteomics)• >10, 000, 000 (LC-MS proteomics)

This is the ‘curse of dimensionality problem’

In 2-D looks good but what happens in:

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• Some methods do not run at all (classical regression)

• Some methods give bad results (KNN, Decision trees)

• Very slow analysis• Very expensive/cumbersome clinical

application• Tends to “overfit”

High-dimensionality (especially with small samples) causes:

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• Over-fitting ( a model to your data)= building a model that is good in original data but fails to generalize well to fresh data

• Under-fitting ( a model to your data)= building a model that is poor in both original data and fresh data

Two (very real and very unpleasant) problems: Over-fitting & Under-fitting

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Intuitive explanation of overfitting & underfitting

• Play the game: find rule to predict who are the instructors in any given class (use today’s class to find a general rule)

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Over/under-fitting are directly related to the complexity of the decision surface and how well the

training data is fit

Predictor X

Outcome of Interest Y

Training Data

Future Data

This line is good!

This line overfits!

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Over/under-fitting are directly related to the complexity of the decision surface and how well the

training data is fit

Predictor X

Outcome of Interest Y

Training Data

Future Data

This line is good!

This line underfits!

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Very Important Concept:

• Successful data analysis methods balance training data fit with complexity. – Too complex signature (to fit training data well)

overfitting (i.e., signature does not generalize)– Too simplistic signature (to avoid overfitting)

underfitting (will generalize but the fit to both the training and future data will be low and predictive performance small).

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B

A

C D E

T

H I J

K

Q L

M N

P O

Part of the Solution: feature selection

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How well supervised learning works in practice?

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Datasets• Bhattacharjee2 - Lung cancer vs normals [GE/DX]• Bhattacharjee2_I - Lung cancer vs normals on common genes between Bhattacharjee2 and

Beer [GE/DX]• Bhattacharjee3 - Adenocarcinoma vs Squamous [GE/DX]• Bhattacharjee3_I - Adenocarcinoma vs Squamous on common genes between

Bhattacharjee3 and Su [GE/DX]• Savage - Mediastinal large B-cell lymphoma vs diffuse large B-cell lymphoma [GE/DX]• Rosenwald4 - 3-year lymphoma survival [GE/CO]• Rosenwald5 - 5-year lymphoma survival [GE/CO]• Rosenwald6 - 7-year lymphoma survival [GE/CO]• Adam - Prostate cancer vs benign prostate hyperplasia and normals [MS/DX]• Yeoh - Classification between 6 types of leukemia [GE/DX-MC]• Conrads - Ovarian cancer vs normals [MS/DX]• Beer_I - Lung cancer vs normals (common genes with Bhattacharjee2) [GE/DX]• Su_I - Adenocarcinoma vs squamous (common genes with Bhattacharjee3) [GE/DX• Banez - Prostate cancer vs normals [MS/DX]

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Methods: Gene and Peak Selection Algorithms• ALL - No feature selection• LARS - LARS• HITON_PC -• HITON_PC_W - HITON_PC+ wrapping phase • HITON_MB - • HITON_MB_W - HITON_MB + wrapping phase • GA_KNN - GA/KNN • RFE - RFE with validation of feature subset with optimized polynomial kernel• RFE_Guyon - RFE with validation of feature subset with linear kernel (as in Guyon)• RFE_POLY - RFE (with polynomial kernel) with validation of feature subset with polynomial optimized kernel• RFE_POLY_Guyon - RFE (with polynomial kernel) with validation of feature subset with linear kernel (as in

Guyon)• SIMCA - SIMCA (Soft Independent Modeling of Class Analogy): PCA based method• SIMCA_SVM - SIMCA (Soft Independent Modeling of Class Analogy): PCA based method with validation of

feature subset by SVM• WFCCM_CCR - Weighted Flexible Compound Covariate Method (WFCCM) applied as in Clinical Cancer

Research paper by Yamagata (analysis of microarray data)• WFCCM_Lancet - Weighted Flexible Compound Covariate Method (WFCCM) applied as in Lancet paper by

Yanagisawa (analysis of mass-spectrometry data)• UAF_KW - Univariate with Kruskal-Walis statistic• UAF_BW - Univariate with ratio of genes between groups to within group sum of squares• UAF_S2N - Univariate with signal-to-noise statistic

57

Classification Performance (average over all tasks/datasets)

58

How well gene selection works in practice?

59

Number of Selected Features (average over all tasks/datasets)

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

8000.00

9000.00

10000.00

ALL

LA

RS

HIT

ON

gp_P

C

HIT

ON

gp_M

B

HIT

ON

gp_P

C_W

HIT

ON

gp_M

B_W

GA

_K

NN

RF

E

RF

E_G

uyon

RF

E_P

OLY

RF

E_P

OLY

_G

uyon

SIM

CA

SIM

CA

_S

VM

WF

CC

M_C

CR

UA

F_K

W

UA

F_B

W

UA

F_S

2N

60

Number of Selected Features (zoom on most powerful methods)

0.00

20.00

40.00

60.00

80.00

100.00

61

Number of Selected Features (average over all tasks/datasets)

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• Special classifiers (with inherent complexity control) combined with feature selection & careful parameterization protocols overcome over-fitting & estimate future performance accurately.

• Caveats: analysis is typically complex and error prone. Need: (a) an experienced analyst on the team, or (b) a validated software system designed for non-experts.

Conclusions so far

Software

• Causal Explorer• Gems • Fast-aims

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Causal Explorer

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• Matlab library of computational causal discovery and variable selection algorithms

• Introductory-level library to our causal algorithms(~3% of our algorithms)• Discover the direct causal or probabilistic relations around a response

variable of interest (e.g., disease is directly caused by and directly causes a set of variables/observed quantities).

• Discover the set of all direct causal or probabilistic relations among the variables.

• Discover the Markov blanket of a response variable of interest, i.e., the minimal subset of variables that contains all necessary information to optimally predict the response variable.

• Code emphasizes efficiency, scalability, and quality of discovery• Requires relatively deep understanding of underlying theory and how the

algorithms operate

Statistics of Registered Users

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• 739 registered users in >50 countries.• 402 (54%) users are affiliated with educational, governmental, and

non-profit organizations• 337 (46%) users are either from private or commercial sectors.• Major commercial organizations that have registered users of Causal

Explorer include:– IBM– Intel– SAS Institute– Texas Instruments– Siemens– GlaxoSmithKline– Merck– Microsoft

Statistics of Registered Users

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Major U.S. institutions that have registered users of Causal Explorer:•Boston University•Brandies University•Carnegie Mellon University•Case Western Reserve University•Central Washington University•College of William and Mary•Cornell University•Duke University•Harvard University•Illinois Institute of Technology•Indiana University-Purdue University Indianapolis•Johns Hopkins University•Louisiana State University•M. D. Anderson Cancer Center•Massachusetts Institute of Technology

•Medical College of Wisconsin•Michigan State University•Naval Postgraduate School•New York University•Northeastern University•Northwestern University•Oregon State University•Pennsylvania State University•Princeton University•Rutgers University•Stanford University•State University of New York•Tufts University•University of Arkansas

•University of California Berkley•University of California Los Angeles•University of California San Diego•University of California Santa Cruz•University of Cincinnati•University of Colorado Denver•University of Delaware•University of Houston-Clear Lake•University of Idaho•University of Illinois at Chicago•University of Illinois at Urbana-Champaign•University of Kansas•University of Maryland Baltimore County•University of Massachusetts Amherst•University of Michigan

•University of New Mexico•University of Pennsylvania•University of Pittsburgh•University of Rochester•University of Tennessee Chattanooga•University of Texas at Austin•University of Utah•University of Virginia•University of Washington•University of Wisconsin-Madison•University of Wisconsin-Milwaukee•Vanderbilt University•Virginia Tech•Yale University

Other systems for supervised analysis of microarray data

Name Version DeveloperAutomatic model selection for

classifier and gene selection methods

ArrayMiner ClassMarker 5.2 Optimal Design, Belgium No

Avadis Prophetic 3.3 Strand Genomics, USA No

BRB ArrayTools 3.2 Beta National Cancer Institute, USA No

caGEDA (accessed 10/2004)University of Pittsburgh and University of Pittsburgh Medical Center, USA

No

Cleaver1.0

(accessed 10/2004)Stanford University, USA No

GeneCluster2 2.1.7Broad Institute, Massachusetts Institute of Technology, USA

No

GeneLinker Platinum 4.5 Predictive Patterns Software, Canada No

GeneMaths XT 1.02 Applied Maths, Belgium No

GenePattern 1.2.1Broad Institute, Massachusetts Institute of Technology, USA

No

Genesis 1.5.0 Graz University of Technology, Austria No

GeneSpring 7 Silicon Genetics, USA No

GEPAS1.1

(accessed 10/2004)National Center for Cancer Research (CNIO), Spain

Limited(for number of genes)

MultiExperiment Viewer 3.0.3 The Institute for Genomic Research, USA No

PAM 1.21a Stanford University, USALimited

(for a single parameter of the classifier)

Partek Predict 6.0 Partek, USALimited

(does not allow optimization of the choice of gene selection algorithms)

Weka Explorer 3.4.3 University of Waikato, New Zeland No

There exist many good software packages for supervised analysis of microarray data, but…

• Neither system provides a protocol for data analysis that precludes overfitting.

• A typical software either offers an overabundance of algorithms or algorithms with unknown performance.

• The software packages address needs only of experienced analysts.

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Purpose of GEMS

GEMS

NormalCancerCancerNormalNormalCancer…CancerCancerNormal

Gene expression data andoutcome variable

ring finger protein 1tubulin, beta, 5glucose-6-phosphate dehydrogenaseglutathione S-transferase M5carnitine acetyltransferaseRho GTPase activating protein 4SMA3mannose phosphate isomerasemitogen-activated protein kinase 3leukotriene A4 hydrolasechromosome 21 open reading frame 1dihydropyrimidinase-like 2beta-2-microglobulindiscs, large (Drosophila) homolog 4

Optional: Gene names & IDs

Cross-validationperformance estimate

Classificationmodel

Rho GTPase activating protein 4SMA3mannose phosphate isomerasemitogen-activated protein kinase 3

Reduced set of genes

Links to literature

(model generation & performance estimation mode)(model generation & performance estimation mode)68

Purpose of GEMS

GEMS

Gene expression data andunknown outcome variable

Classificationmodel

(model application mode)(model application mode)

??????…???

Performance estimate

NormalCancerCancerNormalNormalCancer…CancerCancerNormal

Model predictions

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70

Methods Implemented in GEMSCross-Validation

Designs

N-Fold CV

LOOCVOne-Versus-Rest

One-Versus-One

DAGSVM

Method by WW

Classifiers

Method by CSM

C-S

VM

Accuracy

RCI

PerformanceMetrics

AUC ROC

S2N One-Versus-Rest

S2N One-Versus-One

Non-param. ANOVA

BW ratio

Gene SelectionMethods

HITON_MB

HITON_PC

[a, b]

(x – MEAN(x)) / STD(x)

x / STD(x)

x / MEAN(x)

NormalizationTechniques

x / MEDIAN(x)

x / NORM(x)

x – MEAN(x)

x – MEDIAN(x)

ABS(x)

x + ABS(x)

AUC ROC

HITON_MB

HITON_PC

(x – MEAN(x)) / STD(x)

x / STD(x)

x / MEAN(x)

x / MEDIAN(x)

x / NORM(x)

x – MEAN(x)

x – MEDIAN(x)

ABS(x)

x + ABS(x)

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Software Architecture of GEMS

Wizard-Like User Interface

GEMS 2.0

Generate a classification model

Estimate classification performance

Apply existing model to a new set of patients

Generate a classification model and estimate its performance

Normalization

S2N One-Versus-Rest

S2N One-Versus-One

Non-param. ANOVA

BW ratio

Gene Selection

One-Versus-Rest

One-Versus-One

DAGSVM

Method by WW

Classification byMC-SVM

Method by CS

Cross-Validation Loopfor Performance Est.

N-Fold CV

LOOCV

Report Generator X

Cross-Validation Loopfor Model Selection

N-Fold CV

LOOCV

I

II

I II

Accuracy

RCI

AUC ROC

PerformanceComputation

I

II

HITON_PC

HITON_MB

Computational Engine

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GEMS 2.0: Wizard-Like Interface

Task selection Dataset specification Cross-validation design Normalization

ClassificationGene selectionPerformance metricLogging

Report generation Analysis execution

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GEMS 2.0: Wizard-Like Interface

Input microarray gene expression dataset

File with gene names

File with gene accession numbers

Output model

Statistics of registered users

• 800 users in >50 countries• 350 academic & non-profit users• 450 private & commercial users• 205 scientific citations of major paper that introduced GEMS• Major commercial organizations that have registered users of

Causal Explorer include:– Eli Lilly − Novartis– IBM − GE– Genedata − Nuvera Biosciences– GenomicTree − Cogenetics– Pronota

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FAST-AIMS• FAST-AIMS is a system to support automatic development of

high-quality classification models and biomarker discovery in mass spectrometry proteomics data

• Incorporates automated data analysis protocols of GEMS• Deals with additional challenges of MS data analysis

System Workflow

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Evaluation in multiple user study

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Projects 1

Project Goal & Data types/ design Stage Funding Other involved entities

Development of placebo responder signatures for Irritable Bowel Syndrome

Re-analyze banked samples from clinical trials to create signature of placebo responders; selected proteomic markers and clinical data from humans

In progress

NIH Harvard, NYU

Lung cancer signatures and AKT1 pathway in lung cancer

Find signatures and markers for lung cancer; focus on local pathway around AKT1; human biopsy and cell line array gene expression data

In progress

NIH NYU, Vanderbilt

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Projects 2Molecular signature and biomarkers for atherosclerosis progression and regression

Find signatures, causative, markers, predictive markers and imaging markers; in aortic transplantation and reversa mouse models; microarray and shotgun proteomic data

Transplantation model signature and markers complete; reversa model in development

Industry, NIH (requested)

NYU, Merck

Predicting death from community acquired pneumonia

Find models that predict dire outcomes in patients with CAP; clinical and lab/imaging data

Completed NSF University of Pittsburgh, Carnegie Melon University

Signature for treatment response in colorectal cancer

Develop signature for treatment response in colorectal cancer patients; clinical and gene expression data

In development NIH NYU

Prostate cancer risk and treatment signatures

Develop signature for disease risk and optimal management of prostate cancer patients; clinical and GWAS data

In development Donor funds,DoD (requested)

NYU

Pathways, markers and signatures for pneumonia development in HIV+ subjects

Discover pathways/markers and develop signature for pneumonia risk; clinical and next-gen sequencing microbiomic data

In development NIH (requested)

NYU

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Predicting physician judgment in diagnosis of melanomas& guideline compliance

Predict physicians’ diagnoses and compare to best practice guidelines for compliance; clinical data and automated imaging data

Completed EU University of Trento,Vanderbilt

Predicting risk for sepsis in neonatal intensive unit

Discover optimal treatment strategies; clinical and array gene expression data

In development

NIH (requested)

Vanderbilt, NYU

Proteomic based diagnosis of stroke and stroke-like syndromes

Develop signatures for disease diagnosis; selected proteomic data

Completed Industry

Outcome prediction models for ARDS

Find signatures and markers for ARDS detection and progression; human clinical data from 3 big clinical trials

Completed NIH Vanderbilt

Molecular signatures for treatment response in keloids

Discover pathways/markers and develop signature for treatment response; clinical and microarray data

In development

NIH NYU

80

Projects 3

Publications - new methods development 1Novel local causal network/pathway and biomarker discovery algorithms software and

protocols

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• “Algorithms for Large Scale Markov Blanket Discovery". I. Tsamardinos, C.F. Aliferis, A. Statnikov. In Proceedings of the 16th International Florida Artificial Intelligence Research Society (FLAIRS) Conference, St. Augustine, Florida, USA; AAAI Press, pages 376-380, May 12-14, 2003.

• "Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations". I. Tsamardinos, C.F. Aliferis, A. Statnikov. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA; ACM Press, pages 673-678, August 24-27, 2003.

• "HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection”. C. F. Aliferis, I. Tsamardinos, A. Statnikov. In Proceedings of the 2003 American Medical Informatics Association (AMIA) Annual Symposium, pages 21-25, 2003.

• “Identifying Markov Blankets with Decision Tree Induction.” L. Frey, D. Fisher, I. Tsamardinos, C.F. Aliferis, A. Statnikov. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM), Melbourne, Florida, USA, IEEE Computer Society Press; pages 59-66, November 19-22, 2003.

• "Gene Expression Model Selector (GEMS): a system for decision support and discovery from array gene expression data". A. Statnikov, I. Tsamardinos, Y. Dosbayev, C.F. Aliferis. Int J Med Inform., Aug;74(7-8):491-503, 2005.

• “Factors Influencing the Statistical Power of Complex Data Analysis Protocols for Molecular Signature Development from Microarray Data”. Aliferis CF, Statnikov A, Tsamardinos I, Schildcrout JS, Shepherd BE, Harrell FE. PLoS ONE 2009, 4: e4922.

• "Formative Evaluation of a Prototype System for Automated Analysis of Mass Spectrometry Data". N. Fananapazir, M. Li, D. Spentzos, C.F. Aliferis. Proc AMIA Symposium, 2005.

• “Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part I: Algorithms and Empirical Evaluation” Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D. Koutsoukos (to appear in Journal of Machine Learning Research).

• “Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part II: Analysis and Extensions” Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D. Koutsoukos (to appear in Journal of Machine Learning Research).

• “Design and Analysis of the Causation and Prediction Challenge” I. Guyon, C.F. Aliferis, G.F. Cooper, A. Elisseeff, JP. Pellet, P. Spirtes, A. Statnikov (to appear in Journal of Machine Learning Research).

• “GEMS: A System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data”. Statnikov A, Tsamardinos I, Aliferis CF. AMIA Annual Symposium, 2005.

• “Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data”. Statnikov A, Tsamardinos I, Aliferis CF. Twelfth National Conference on Artificial Intelligence (AAAI), 2005.

• “Using GEMS for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data”. Statnikov A, Tsamardinos I, Aliferis CF. Thirteenth Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2005.

Publications -new methods development 2 Network reverse engineering/global causal discovery

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• “A Novel Algorithm for Scalable and Accurate Bayesian Network Learning”. L.E. Brown, I. Tsamardinos, C.F. Aliferis. In Proceedings of the 11th World Congress on Medical Informatics (MEDINFO), San Francisco, California, USA; September 7-11, 2004.

• “A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms” Laura E. Brown, Ioannis Tsamardinos, C. F. Aliferis. Proc AAAI Conference, 2005.

• “The Max-Min Hill Climbing Bayesian Network Structure Learning Algorithm”. I. Tsamardinos, L.E. Brown, C.F. Aliferis. Machine Learning, 65:31-78, 2006.

• “A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data”. S. Mani and C. Aliferis. ”AI in medicine Europe (AIME) Conference, Amsterdam, July 2007.

• “Learning Causal and Predictive Clinical Practice Guidelines from Data”. S. Mani, C. F. Aliferis, S. Krishnaswami, T. Kotchen. In International Medical Informatics Congress, MEDINFO, 2007.

• “Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part I: Algorithms and Empirical Evaluation” Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D. Koutsoukos (to appear in Journal of Machine Learning Research).

Publications - new methods development 3Theoretical properties of discovery methods

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• "Towards Principled Feature Selection: Relevance, Filters, and Wrappers". I. Tsamardinos and C.F. Aliferis. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, Florida, USA, January 3-6, 2003.

• "Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation Of Explanatory Factors". C.F. Aliferis, I. Tsamardinos, P. Massion, A. Statnikov, D. Hardin. In Proceedings of the 2003 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS), Las Vegas, Nevada, USA; CSREA Press, June 23-26, 2003.

• "A Theoretical Characterization of Linear SVM-Based Feature Selection". D. Hardin, I. Tsamardinos, C.F. Aliferis. In Twenty-First International Conference on Machine Learning (ICML), 2004.

• “Are Random Forests Better than Support Vector Machines for Microarray-Based Cancer Classification?” A. Statnikov, C.F. Aliferis. Proc AMIA Fall Symposium 2007.

• “Using SVM Weight-Based Methods to Identify Causally Relevant and Non-Causally Relevant Variables”. Statnikov A., Hardin D., Aliferis CF. Workshop on: Feature Selection and Causality, NIPS, 2006.

• “Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective”. Aliferis CF, Statnikov A, Tsamardinos I. Cancer Informatics, 2: 133–162, 2006.

• “ Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part II: Analysis and Extensions” Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D. Koutsoukos (to appear in Journal of Machine Learning Research).

• “The Problem of Statistical Gene Instability in Microarray Studies: External Reproducibility and Biological Importance of Unstable Genes and their Molecular Signatures” C.F. Aliferis, A. Statnikov, S. Pratap, E. Kokkotou. (In preparation).

• “Application and Comparative Evaluation of Causal and Non-Causal Feature Selection Algorithms for Biomarker Discovery in High-Throughput Biomedical Datasets”. Aliferis CF, Statnikov A., Tsamardinos I, Kokkotou E, Massion PP. Workshop on Feature Selection and Causality, NIPS 2006.

• “Pathway induction and high-fidelity simulation for molecular signature and biomarker discovery in lung cancer using microarray gene expression data”. Aliferis CF, Statnikov A, Massion P. In Proc 2006 American Physiological Society Conference: Physiological Genomics and Proteomics of Lung Disease. November 2-5, 2006.

Publications - new methods development 4 Other methodological studies with relevance for predictive modeling

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• “Temporal Representation Design Principles: An Assessment in the Domain of Liver Transplantation”. C.F. Aliferis, and G. F. Cooper. Proc AMIA Symp., 170-4, 1998.

• “Machine Learning Models For Lung Cancer Classification Using Array Comparative Genomic Hybridization”. C. F. Aliferis, D. Hardin, P.Massion. Proc AMIA Symp., 7-11, 2002.

• "Machine Learning Models For Classification Of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data". C.F. Aliferis, I. Tsamardinos, P. Massion, A. Statnikov, N. Fananapazir, D. Hardin. In Proceedings of the 16th International Florida Artificial Intelligence Research Society (FLAIRS) Conference, St. Augustine, Florida, USA; AAAI Press, pages 67-71, May 12-14, 2003.

• "Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine." Y. Aphinyanaphongs, C.F. Aliferis. In Proceedings of the 2003 American Medical Informatics Association (AMIA) Annual Symposium, Washington, DC, USA; pages 31-35, 2003.

• “Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine”. Y. Aphinyanaphongs, I. Tsamardinos, A. Statnikov, D. Hardin, C.F. Aliferis. J Am Med Inform Assoc., Mar-Apr;12(2):207-16, 2005.

• “A Semantic Model for Organizing Molecular Medicine "Omics" Modalities and Evidence” Firas Wehbe, Pierre Massion, Cindy Gadd, Daniel Masys and C.F. Aliferis. (to appear in Cancer Informatics).

Benchmarking Studies 1Evaluation of classifier algorithms

• Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 2005, 21: 631-643.

• Statnikov A, Aliferis CF, Tsamardinos I: Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development. Medinfo 2004 2004, 11: 813-817.

• Statnikov A, Wang L, Aliferis CF: A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008, 9: 319.

Evaluation of biomarker/variable selection algorithms• Aliferis CF, Tsamardinos I, Statnikov A: HITON: a novel Markov blanket algorithm for optimal variable

selection. AMIA 2003 Annual Symposium Proceedings 2003, 21-25.• Aliferis CF, Statnikov A, Massion PP: Pathway induction and high-fidelity simulation for molecular

signature and biomarker discovery in lung cancer using microarray gene expression data . Proceedings of the 2006 American Physiological Society Conference "Physiological Genomics and Proteomics of Lung Disease" 2006.

• Aliferis CF, Statnikov A, Tsamardinos I, Kokkotou E, Massion PP: Application and comparative evaluation of causal and non-causal feature selection algorithms for biomarker discovery in high-throughput biomedical datasets. Proceedings of the NIPS 2006 Workshop on Causality and Feature Selection 2006.

• Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part II: Analysis and Extensions. Journal of Machine Learning Research 2009.

• Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD: Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification. Part I: Algorithms and Empirical Evaluation. Journal of Machine Learning Research 2009.

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Benchmarking Studies 2

Comparison of algorithms for extraction of all maximally predictive and non-redundant molecular signatures

• Statnikov A: Algorithms for Discovery of Multiple Markov Boundaries: Application to the Molecular Signature Multiplicity Problem. Ph D Thesis, Department of Biomedical Informatics, Vanderbilt University 2008.

Comparison of protocols to detect predictive signal of prognostic molecular signatures

• Aliferis CF, Statnikov A, Tsamardinos I, Schildcrout JS, Shepherd BE, Harrell FE: Factors Influencing the Statistical Power of Complex Data Analysis Protocols for Molecular Signature Development from Microarray Data. PLoS ONE 2009, 4: e4922.

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Patents

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1. Aliferis CF, Tsamardinos I. Method, system, and apparatus for casual discovery and variable selection for classification. U.S. patent (US 7,117,185 B1).

2. Aliferis CF. Local Causal and Markov Blanket Induction Methods for Causal Discovery and Feature Selection from Data. U.S. provisional patent application (61149758).

3. Statnikov A, Aliferis CF. Methods for Discovery of Markov Boundaries from Datasets with Hidden Variables. U.S. provisional patent application (61145652).

4. Statnikov A, Aliferis CF. A Method for Determining All Markov Boundaries and Its Application for Discovering Multiple Optimally Predictive and Non-Redundant Molecular Signatures. U.S. provisional patent application.

5. Statnikov A, Aliferis CF, Tsamardinos I, Fananapazir N. Method and System for Automated Supervised Data Analysis. U.S. patent application (20070122347).

Grants 1

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• NIH/NLM 2, R56 LM007948-04A1, Aliferis, PI, Causal Discovery Algorithms for Translational Research with High -Throughput Data, 07/15/2008 – 07/14/2009 , $333,863.00

• NSF, NSF 0725746 , Guyon, PI, Causal Discovery Workbench and Challenge Program , 09/01/2007- 08/30/2009 • NIH/NCRR, 1 U54 RR024386-01A2, Cronstein, PI, Institutional Clinical and Translational Science Award ,

07/01/2009 – 06/30/2014, $32,411,416.00 • NIH/NCCAM, 1 R01 AT004662-01A1, Kokkotou, PI, Omics and Variable Responses to Placebo and Acupuncture

in Irritable Bowel Syndrome , 07/01/2009 – 06/30/2014 , $376,663.00 • NIH/NHLBI, 1 R01 HL089800-01A1, Schuening, PI , Genomics and Genetics of Acute Graft-Versus Host Disease,

07/01/2009 – 06/30/2014, $2,489,743.00 • NSF, Guyon, PI, Resource Allocation Strategies in Causal Modeling• DOD, PC093319P1, Aliferis, PI, Enhanced prediction of prostate cancer risk and progression and causative gene

identification Award Mechanism: Synergistic Idea Development Award, 07/01/2010 – 06/30/2013, $750.000.00 • NIH, 1 S10 RR029683-01, Smith, PI, High Performance Computing Equipment to Support Biomedical Research at

NYU, 12/02/2009 – 11/30/20010, $650,433.00 • NIH, 1R01OD006352-01, Fisher, PI, Key Factors for the Regression and Imaging of Atherosclerosis, 09/01/2009 -

08/31/2014 • NIH, RO1 AR0566672, Cronstein, PI, The Pharmacology of Dermal Fibrosis, 09/01/09 - 08/30/2011,

$295,242.00 • NIH, Blumenberg, PI, Skinomics , 09/01/2009-08/31/2011, 582,596.00 • NIH, 1U01HL098959-01, Weiden, PI, Bacterial, Fungal and Viral Microbiome in the Lung, 9/30/2009 – 9/29/2014 • NIH, RFA-OD-09-005, Aliferis, PI, Recovery Act Limited Competition: Supporting New Faculty Recruitment to

Enhance Research Resources through Biomedical Research Core Centers (P30), 09/30/2009 – 06/20/2010 • NIH, Jiyoung, PI, Integrative Analysis of Genome-wide Gene Expression for Prostate Cancer Prognosis • NIH, Cllelland, PI, First Episode Psychiatric Illness: A Clinical, BioData and Biomaterials Resource

Grants 2• NIH/NHLBI 1 U01 HL081332-01 (Ware), “Biomarker Profiles in the Diagnosis/Prognosis of ARDS”, 08/12/2005 – 06/30/2009

Total Award: $6,059,257.• NIH, NCI 1 U24 CA126479-01 (Liebler), “Clinical Proteomic Technology Assessment for Cancer”, 09/28/2006 – 08/31/2011,

Total Award: $7,388,990. • NSF 0725746 (Guyon), “Causal Discovery Workbench and Challenge Program”, 08/15/2007 – 07/31/2009, Total Award:

$107,721.• NIH, NLM 2 T15 LM007450-06 (Gadd) “Vanderbilt Biomedical Informatics Training Program”, 07/01/2007 – 06/30/2012,

Total Award: $3,969,225. • NIH/NLM, 1 R01 LM007948-01 “Principled methods for very-large-scale causal discovery.” 07/01/2003 – 06/30/2006. Total

Award: $631,180.• NIH/NLM BISTI Planning Grant, 1 P20 LM007613-01 (Stead) “Pilot Project Computational Models of Lung Cancer:

Connecting Classification, Gene Selection, and Molecular Sub-typing”, 09/01/2002 – 08/30/2004, Total Award: $226,500.• NIH/NLM 1 T15 LM07450-01 (Miller), “Biomedical Informatics Training Grant.” 07/01/2002 – 06/30/2007, Total Award:

$3,966,644.• BMS training contract, Fall-Spring 2002, Total Award: $150,000.• “Vanderbilt Academic Venture Capital Fund support for the Discovery Systems Laboratory”, 07/01/2003 – 06/30/2006, Total

Award: $846,3471.• “Vanderbilt University Discovery Grant to study Complex Modeling of Clinical Trial Data with Gene Expression Covariates &

Development of Optimal Re-analysis Policies” 07/01/2001 – 06/30/2002, Total Award: $50,000.• “Causal Discovery Challenge” from PASCAL (Pattern Analysis, Statistical Modeling and Computational Learning). PASCAL is

the European Commission's IST-funded Network of Excellence for Multimodal Interfaces, 03/01/2006 – 11/30/2007, Total Award: 18,000 Euros.

• NIH/NCI 1 P50 CA095103-01 (Coffey) “SPORE in GI Cancer” 09/24/2002 – 04/30/2007, Total Award: $11,851,282.• NIH/NHLBI 1 U01 HL65962-01A1 (Roden) “Pharmacogenics of Arrhythmia Therapy”, 04/01/2001 – 03/31/2005, Total Award:

$11,189,918.• NIH/NCI 1 P50 CA98131-01 (Arteaga) “SPORE in Breast Cancer” 08/01/2003 – 05/31/2008, Total Award: $12,804,130.

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Main points, session #1• Molecular signatures are an important tool for research both

basic and translational; they are finding their way to clinical practice

• Data analytics of molecular signatures are very important• We introduced the importance of bioinformatics for the

analysis of high dimensional data and the creation of molecular signatures

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For session #2• Review slides in today’s presentation and bring written

questions (if any) to discuss in subsequent sessions• Read materials regarding assay technologies (to be

distributed electronically). Note:– Basic principle underlying each technology– Advantages over older technologies– Limitations & technical difficulties– How it may support your research interests now or in the future

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