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Introduction to translational and clinical bioinformatics Connecting complex molecular information to clinically relevant decisions using molecular.

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1 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 Alexander Statnikov Ph.D. Director, Computational Causal Discovery laboratory Assistant Professor, NYU Center for Health Informatics and Bioinformatics, General Internal Medicine

2 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

3 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

4 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: - s/names/interests - adjustments to plan

5 NYU Center for health Informatics & Bioinformatics: Broad Plan
Infrastructure & Integrative Methods/Activities Bioinformatics Educational informatics CTSI BPIC (best Practices Integrative Consultation Core/Service Microarray Informatics: Upstream Differential expression, Pathway inference Molecular profiles Evidence based medicine, and Information retrieval Informatics High Performance Computing Facility Literature Synthesis & Benchmarking studies Research labs Kluger Molecular Signatures EBM, IR & Scientometrics Computational Causal Discovery Method-problem “matchmaking” Library Collaborative Design and execution of studies Next-gen sequencing informatics : Upstream analyses i. Chi-seq ii. RNA seq iii. Epigenetics iv. Microbiomics v. micro RNA studies vi. CNV & splice variation studies vii. Digital RNA viii. Denovo sequencing & re-sequencing Downstream analyses 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 Cancer Center MS/PhD (& Post-doc Fellowship) Program Genetics-Genomics Continuing Education Workshops & tutorials Paper digest Research Colloquium Invited Speakers COEs Multi-modal & Integrative studies Integrate/Focus Existing Informatics and Increase Collaborations Proteomics Informatics

6 Current Capabilities: Areas
Health Informatics Infrastructure & Integrative Methods/Activities Bioinformatics Educational informatics CTSI BPIC (best Practices Integrative Consultation Core/Service Microarray Informatics: Upstream Differential expression, Pathway inference Molecular profiles Evidence based medicine, and Information retrieval Informatics High Performance Computing Facility Literature Synthesis & Benchmarking studies Research labs Kluger Molecular Signatures EBM, IR & Scientometrics Computational Causal Discovery Method-problem “matchmaking” Library Collaborative Design and execution of studies Next-gen sequencing informatics : Upstream analyses i. Chi-seq ii. RNA seq iii. Epigenetics iv. Microbiomics v. micro RNA studies vi. CNV & splice variation studies vii. Digital RNA viii. Denovo sequencing & re-sequencing Downstream analyses 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 Cancer Center MS/PhD (& Post-doc Fellowship) Program Genetics-Genomics Continuing Education Workshops & tutorials Paper digest Research Colloquium Invited Speakers COEs Multi-modal & Integrative studies Integrate/Focus Existing Informatics and Increase Collaborations Proteomics Informatics

7 Current & Future capabilities
Health Informatics Educational informatics Content management, medical simulations Evidence based medicine, and Information retrieval Informatics 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” Library Collaborative Apply, evaluate, refine next-gen IR methods 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 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

8 Current & Future capabilities
Infrastructure & Integrative Methods/Activities CTSI (supported by rest of objectives) High Performance Computing Facility Sequencing server; hectar1; hectar2; Funds; needs; grants; personnel post; specs; room/networking/access; Personnel hires; hw install; licenses; BP; launch Research labs Kluger Molecular Signatures EBM, IR & Scientometrics Computational Causal Discovery 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 MS/PhD (& Post-doc Fellowship) Program Formal Training in Biomedical Informatics at pre and post-doctoral levels Continuing Education Workshops & tutorials Paper digest Research Colloquium Invited Speakers Continuing Education Workshops & tutorials Paper digest Research Colloquium Invited Speakers Integrate/Focus Existing Informatics and Increase Collaborations Faculty and Staff career development; Informatics Affiliates; Working Collaborations with Courant, Polytechnic, NYC Informatics and other non-NYUMC entities

9 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 Area-specific (Disease, Assay) Informatics 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… Genetics-Genomics COEs Sequencing Informatics: Chip-Seq analysis, digital gene expression, de novo sequence assembly & reassembly, CNV analysis, epigenomic studies, microbiomics Cancer Center Proteomics Informatics: platform-specific pre-processing, differential abundance, peptide-protein mapping, protein identification, de novo protein interaction network inference, protein modification and structure studies,… 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

10 Summary Contacts (Until Centralized Consultation Service is Launched)
Management of Clinical and protocol data  James Robinson Educational Informatics  Mark Triola Next-Gen Information Retrieval  Lawrence Fu, Constantin Aliferis, TBD Informatics for Data Mining  Alexander Statnikov, Constantin Aliferis Data Integration & Warehousing  John Chelico, Ross Smith, Constantin Aliferis High Performance Computing  Constantin Aliferis, Ross Smith Best Practices in Bioinformatics  Constantin Aliferis, Alexander Statnikov Sequencing Informatics  Upstream: Stuart Brown, Alexander Alekseyenko, Yuval Kluger, Jinhua Wang, TBD, TBD Downstream: Alexander Alekseyenko, Yuval Kluger, Jinhua Wang, Alexander Statnikov, Constantin Aliferis Microarray Informatics  Jiri Zafadil, Yuval Kluger, Jinhua Wang, Constantin Aliferis, Alexander Statnikov Cancer Informatics  Yuval Kluger, Jinhua Wang, Stuart Brown, Jiri Zafadil, Constantin Aliferis Proteomics Informatics  Stuart Brown, Jinhua Wang, Constantin Aliferis, Alexander Statnikov, TBD General Tools  Stuart Brown Specialized applications (Genetics, Regulation, Pathways…)  Stuart Brown, Yuval Kluger, Alexander Statnikov, Constantin Aliferis Molecular Signatures development, biomarker discovery, Multi-modal and Integrative studies  Constantin Aliferis, Alexander Statnikov, Yuval Kluger

11 Molecular Signatures Definition =
computational or mathematical models that link high-dimensional molecular information to phenotype of interest

12 Molecular Signatures Gene markers New drug targets

13 Molecular Signatures: Main Uses
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) 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 Ancillary benefits 2: Discovery of structure & mechanisms (regulatory/interaction networks, pathways, sub-types)

14 Molecular Signatures The 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 classification 2. “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),

15 Less Conventional Uses of Molecular Signatures
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 …?

16 Recent molecular mignatures available for patient care
Agendia Clarient Prediction Sciences LabCorp OvaSure University Genomics Genomic Health Veridex BioTheranostics Applied Genomics Power3 Correlogic Systems

17 Molecular signatures in the market (examples)
Company Product Disease Purpose Agendia MammaPrint Breast cancer Risk assessment for the recurrence of distant metastasis in a breast cancer patient. TargetPrint Quantitative determination of the expression level of estrogen receptor, progesteron receptor and HER2 genes. This product is supplemental to MammaPrint. CupPrint Cancer Determination of the origin of the primary tumor. University Genomics Breast Bioclassifier Classification of ER-positive and ER-negative breast cancers into expression-based subtypes that more accurately predict patient outcome. Clarient Insight Dx Breast Cancer Profile Prediction of disease recurrence risk. Prostate Gene Expression Profile Prostate cancer Diagnosis of grade 3 or higher prostate cancer. Prediction Sciences RapidResponse c-Fn Test Stroke Identification 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 Individualized 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 Classification of 39 types of cancer. Breast Cancer Index Risk 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. PulmoType Lung cancer Classification of non-small cell lung cancer into adenocarcinoma versus squamous cell carcinoma subtypes. PulmoStrat Assessment of an individual's risk of lung cancer recurrence following surgery for helping with adjuvant therapy decisions. Correlogic OvaCheck Ovarian cancer Early detection of epithelial ovarian cancer. LabCorp OvaSure Assessment of the presence of early stage ovarian cancer in high-risk women. Veridex GeneSearch BLN Assay Determination of whether breast cancer has spread to the lymph nodes. Power3 BC-SeraPro Differentiation between breast cancer patients and control subjects.

18 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”.

19 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

20 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:

21 Oncotype DX Development 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): • 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: • Oncotype DX did not undergo FDA review. Here is an article that mentions FDA review of Oncotype DX (slightly outdated): • The following paper shows the health benefits and cost-effectiveness benefits of using Oncotype DX:

22 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

23 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)

24 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

25 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

26 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.

27 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.

28 Key ingredients for developing a molecular signature
Well-defined clinical problem & access to patients Computational & Biostatistical Analysis Molecular Signature High-throughput assays

29 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!

30 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 patients

31 Problem with OvaCheck A B C D E F Figure from Baggerly et al
(Bioinformatics, 2004) D E F

32 Molecular Signatures Gene markers New drug targets

33 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

34 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

35 An early kind of analysis: learning disease sub-types by clustering patient profiles
Rb

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

37 E.g., for treatment Respond to treatment Tx1 p53 Do not
Rb

38 E.g., for diagnosis Adenocarcinoma p53 Squamous carcinoma Rb

39 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

40 Unfortunately clustering is a non-specific method and falls into the ‘one-solution fits all’ trap when used for prediction Do not Respond to treatment Tx2 p53 Rb Respond to treatment Tx2

41 Clustering is also non-specific when used to discover pathway membership, regulatory control, or other causation-oriented relationships 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 G1 G2 Ph G3

42 Two improved classes of methods
Supervised learning  predictive signatures and markers Regulatory network reverse engineering  pathways

43 Supervised learning : use the known phenotypes (a. k
Supervised learning : use the known phenotypes (a.k.a “labels) in training data to build signatures or find markers highly specific for that phenotype

44 Regulatory network reverse engineering

45 Supervised learning: a geometrical interpretation

46 In 2-D looks good but what happens in:
10,000-50,000 (regular gene expression microarrays, aCGH, and early SNP arrays) >500,000 (tiled microarrays, new SNP arrays) 10, ,000 (regular MS proteomics) >10, 000, 000 (LC-MS proteomics) This is the ‘curse of dimensionality problem’

47 High-dimensionality (especially with small samples) causes:
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”

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

49 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)

50 Over/under-fitting are directly related to the complexity of the decision surface and how well the training data is fit Outcome of Interest Y This line is good! This line overfits! Training Data Future Data Predictor X

51 Over/under-fitting are directly related to the complexity of the decision surface and how well the training data is fit Outcome of Interest Y This line is good! This line underfits! Training Data Future Data Predictor X

52 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).

53 Part of the Solution: feature selection
B A C D E T H I J K Q L M N P O

54 How well supervised learning works in practice?

55 Datasets Bhattacharjee2 - Lung cancer vs normals [GE/DX]
Bhattacharjee2_I - Lung cancer vs normals on common genes between Bhattacharjee2 and Beer [GE/DX] Bhattacharjee 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] Rosenwald year lymphoma survival [GE/CO] Rosenwald year lymphoma survival [GE/CO] Rosenwald 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]

56 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)

60 Number of Selected Features (zoom on most powerful methods)

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

62 Conclusions so far 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.

63 Software Causal Explorer Gems Fast-aims

64 Causal Explorer 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

65 Statistics of Registered Users
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

66 Statistics of Registered Users
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

67 Other systems for supervised analysis of microarray data
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. To point #2: Thus is it not clear to the user how to choose an optimal algorithm for a given data analysis task. To point #3: However, there is a need to use this software (and still achieve good results) by users who know little about data analysis (e.g., biologists and clinicians). We built a system that addresses all above problems.

68 Purpose of GEMS GEMS (model generation & performance estimation mode)
Cross-validation performance estimate Classification model Reduced set of genes Links to literature Gene expression data and outcome variable GEMS Optional: Gene names & IDs (model generation & performance estimation mode)

69 Purpose of GEMS GEMS (model application mode) Performance
Gene expression data and unknown outcome variable Performance estimate Model predictions GEMS Classification model (model application mode)

70 Methods Implemented in GEMS
Cross-Validation Designs N-Fold CV LOOCV One-Versus-Rest One-Versus-One DAGSVM Method by WW Classifiers Method by CS MC-SVM Gene Selection Methods Normalization Techniques S2N One-Versus-Rest [a, b] S2N One-Versus-One (x – MEAN(x)) / STD(x) (x – MEAN(x)) / STD(x) Non-param. ANOVA x / STD(x) x / STD(x) BW ratio x / MEAN(x) x / MEAN(x) HITON_MB HITON_MB Performance Metrics x / MEDIAN(x) x / MEDIAN(x) HITON_PC HITON_PC x / NORM(x) x / NORM(x) Accuracy x – MEAN(x) x – MEAN(x) RCI x – MEDIAN(x) x – MEDIAN(x) AUC ROC AUC ROC ABS(x) ABS(x) x + ABS(x) x + ABS(x)

71 Software Architecture of GEMS
Wizard-Like User Interface 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 by MC-SVM Method by CS Cross-Validation Loop for Performance Est. N-Fold CV LOOCV Report Generator X for Model Selection I II Accuracy RCI AUC ROC Performance Computation HITON_PC HITON_MB Computational Engine

72 GEMS 2.0: Wizard-Like Interface
Task selection Dataset specification Cross-validation design Normalization Logging Performance metric Gene selection Classification Report generation Analysis execution

73 GEMS 2.0: Wizard-Like Interface
Input microarray gene expression dataset File with gene names File with gene accession numbers Output model

74 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

75 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

76 System Workflow

77 Evaluation in multiple user study
FAST-AIMS: Expert: 0.811 Higher than PSA: [Thompson JAMA 2005]

78 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 NYU, Vanderbilt

79 Projects 2 Molecular 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 Donor funds, DoD (requested) 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

80 Projects 3 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 Industry Outcome prediction models for ARDS Find signatures and markers for ARDS detection and progression; human clinical data from 3 big clinical trials NIH Molecular signatures for treatment response in keloids Discover pathways/markers and develop signature for treatment response; clinical and microarray data

81 Publications - new methods development 1 Novel local causal network/pathway and biomarker discovery algorithms software and protocols “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 , 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 , 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): , 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.

82 Publications -new methods development 2 Network reverse engineering/global causal discovery
“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).

83 Publications - new methods development 3 Theoretical properties of discovery methods
"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.

84 Publications - new methods development 4 Other methodological studies with relevance for predictive modeling “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).

85 Benchmarking Studies 1 Evaluation 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: 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 , 11: 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, 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 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.

86 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.

87 Patents Aliferis CF, Tsamardinos I. Method, system, and apparatus for casual discovery and variable selection for classification. U.S. patent (US 7,117,185 B1). Aliferis CF. Local Causal and Markov Blanket Induction Methods for Causal Discovery and Feature Selection from Data. U.S. provisional patent application ( ). Statnikov A, Aliferis CF. Methods for Discovery of Markov Boundaries from Datasets with Hidden Variables. U.S. provisional patent application ( ). 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. Statnikov A, Aliferis CF, Tsamardinos I, Fananapazir N. Method and System for Automated Supervised Data Analysis. U.S. patent application ( ).

88 Grants 1 NIH/NLM 2, R56 LM A1, Aliferis, PI, Causal Discovery Algorithms for Translational Research with High -Throughput Data, 07/15/2008 – 07/14/2009 , $333,863.00 NSF, NSF , Guyon, PI, Causal Discovery Workbench and Challenge Program , 09/01/ /30/2009 NIH/NCRR, 1 U54 RR A2, Cronstein, PI, Institutional Clinical and Translational Science Award , 07/01/2009 – 06/30/2014, $32,411,416.00 NIH/NCCAM, 1 R01 AT A1, 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 HL A1, 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, $ NIH, 1 S10 RR , Smith, PI, High Performance Computing Equipment to Support Biomedical Research at NYU, 12/02/2009 – 11/30/20010, $650,433.00 NIH, 1R01OD , Fisher, PI, Key Factors for the Regression and Imaging of Atherosclerosis, 09/01/ /31/2014 NIH, RO1 AR , Cronstein, PI, The Pharmacology of Dermal Fibrosis, /01/ /30/2011, $295,242.00 NIH, Blumenberg, PI, Skinomics , 09/01/ /31/2011, 582,596.00 NIH, 1U01HL , Weiden, PI, Bacterial, Fungal and Viral Microbiome in the Lung, 9/30/2009 – 9/29/2014 NIH, RFA-OD , 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

89 Grants 2 NIH/NHLBI 1 U01 HL (Ware), “Biomarker Profiles in the Diagnosis/Prognosis of ARDS”, 08/12/2005 – 06/30/2009 Total Award: $6,059,257. NIH, NCI 1 U24 CA (Liebler), “Clinical Proteomic Technology Assessment for Cancer”, 09/28/2006 – 08/31/2011, Total Award: $7,388,990. NSF (Guyon), “Causal Discovery Workbench and Challenge Program”, 08/15/2007 – 07/31/2009, Total Award: $107,721. NIH, NLM 2 T15 LM (Gadd) “Vanderbilt Biomedical Informatics Training Program”, 07/01/2007 – 06/30/2012, Total Award: $3,969,225. NIH/NLM, 1 R01 LM “Principled methods for very-large-scale causal discovery.” 07/01/2003 – 06/30/ Total Award: $631,180. NIH/NLM BISTI Planning Grant, 1 P20 LM (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 LM (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 CA (Coffey) “SPORE in GI Cancer” 09/24/2002 – 04/30/2007, Total Award: $11,851,282. NIH/NHLBI 1 U01 HL A1 (Roden) “Pharmacogenics of Arrhythmia Therapy”, 04/01/2001 – 03/31/2005, Total Award: $11,189,918. NIH/NCI 1 P50 CA (Arteaga) “SPORE in Breast Cancer” 08/01/2003 – 05/31/2008, Total Award: $12,804,130.

90 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

91 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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