Medical Informatics and Bioinformatics in …Biomedical Informatics Laboratory STBIO Panel Discussions - Snapshot of Translational Biomedical Informatics • Informatics for Genome-Phenome

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© C. A. Kulikowski – Rutgers University

Biomedical Informatics Laboratory

Medical InformaticsMedical Informatics

and Bioinformatics and Bioinformatics

in Translational Medicinein Translational Medicine

Casimir A. Kulikowski, Ph.D.Casimir A. Kulikowski, Ph.D.Board of Governors Professor of Computer ScienceBoard of Governors Professor of Computer Science

Rutgers University, New Brunswick, New Jersey, USARutgers University, New Brunswick, New Jersey, USA

Perspectives on Medical Informatics WorkshopPerspectives on Medical Informatics Workshop

Heidelberg, Heidelberg, imim Neuenheimer Neuenheimer FeldFeld, April 5, 2008, April 5, 2008



Bioinformatics and Medical Bioinformatics and Medical

Informatics Informatics

• Bioinformatics key to

making the Human

Genome Project possible

• Increasingly promising in

enabling and scaling

omics technologies for

translational impact on

healthcare

• Medical Informatics central to scalability of health care systems, clinical research, and education

• Web-based knowledge sources and search technologies, standardized documentation and decision support

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Expectations Expectations

• Bioinformatics will

translate the flood of

omic data into

personalized medicine

for patients

• Medical Informatics

promises universal

technologies for

healthcare, making

evidence-based and

preventive medicine

routine



Many Translational Opportunities Many Translational Opportunities

for for BionformaticsBionformatics

• Rapid growth of -omics data and proliferation of genomic and proteomics data bases

• Increasing number of analysis methods, software, servers for integrating heterogeneous data

• Interconnected and semantically normalized linkages between heterogeneous data sources

• Phenotypic clinical data increasingly standardized through Electronic Health Records (EHRs)

• Consumer-oriented commercial personal “whole”-genome datasets (from very minimal to fairly reasonable coverage depending on cost)

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But, an abundance of challenges alsoBut, an abundance of challenges also

• Data capture for genotype, phenotype and intermediate data still very site-dependent

• Data unreliable and hard to normalize - few generalized, reliable reference sets

• Complementarities/redundancies/alignment of multimodal datasets (sequence, structure, function, imaging) not easy to determine from literature on prior studies

• Data analysis and integration methods complex, nonstandardized, hard to understand and evaluate for non-specialists.

• Semantic web still in its infancy

• Underlying omics is an ever-shifting science and technology of unprecedented size, scale, and diversity…



What is new in translational biomedical What is new in translational biomedical

informatics?informatics?

• 1st AMIA Summit on Bioinformatics in March 2008

• Highlights of research advances from translational bioinformatics and medical informatics

• Identification of “grand challenges” for both fields

• Relationship to underlying content and method disciplines

• Questions about information quality, reliability, processing, dissemination, education and training

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Selective Highlights from AMIA STBIO Selective Highlights from AMIA STBIO

Meeting, just heldMeeting, just held

in March 2008 in March 2008 -- first ever meeting on first ever meeting on

Translational BioinformaticsTranslational Bioinformatics



Four Tracks at AMIA STBIO:Four Tracks at AMIA STBIO:

• T1: Informatics Methods for the Analysis of

Molecular and Clinical Data

• T 2: Relating and Representing Phenotypes and

Disease

• T 3: Dissecting Disease through the Study of

Organisms, Evolution, and Taxonomy

• T 4: Computational Approaches to Finding Molecular

Mechanisms and Therapies for Disease

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Keynote Speaker Highlights NIH Keynote Speaker Highlights NIH

ProgramsPrograms• Alan Krensky, Director of the new Office of Portfolio

Analysis and Strategic Initiatives (OPASI), Deputy Director,

NIH - Researcher on T- lymphocytes in disease - gave

overview of NIH programs in translational bioinformatics:

• NCBCs (National Centers for Biomedical Computing)

• CTSAs (Clinical and Translational Sciences Award)

• caBIG (Cancer Biomedical Informatics Grid)

• BIRN (Biomedical Informatics Research Network)



NIH National Centers for NIH National Centers for

Biomedical Computing (Biomedical Computing (NCBCsNCBCs))

• Major networks of investigators connected at major centers

and collaborating groups nationwide • Stanford SIMBIOS - Simulation of Biological Structures, Altman;

• Michigan NCIBI - National Ctr Integrative Biomedical Informatics, Athey;

• Columbia National Ctr for Multi-Scale Analysis of Gen and Cell Network

MAGNet, Floratos/Califano;

• BWH-Harvard NA-MIC Nat Alliance for Medical Imaging Computing,

Kikinis;

• BWH-CH-Harvard i2b2 - Informatics for Integrating Biology to Bedside,

Kohane;

• Stanford NCBC - National Center for Biomedical Ontology, Mussen;

• UCLA CCB -Center for Computational Biology, Toga.

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Example: Population Studies from MR Example: Population Studies from MR

Brain Imaging Brain Imaging --> Probabilistic Brain > Probabilistic Brain

Function Atlases (Toga, UCLA CCB)Function Atlases (Toga, UCLA CCB)



STBIO Panel Discussions STBIO Panel Discussions -- Snapshot of Snapshot of

Translational Biomedical InformaticsTranslational Biomedical Informatics

• Informatics for Genome-Phenome Correlation Using De-Identified Specimens and EMR data (preliminary promising trial from Vanderbilt)

• Government regulation in decision support (none if open loop DS with physician intervention)

• Clinical Trials international collaborative network of the Immune Tolerance Network (good progress)

• Translational Imaging Informatics (imaging and bioinformatics advancing individually, but only early interactions)

• HealthGrid (caBIG experiences on scaling)

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More Snapshots from STBIO PanelsMore Snapshots from STBIO Panels

• High Dimensionality Data in Translational Medicine(many advanced results reported by Michigan NCIBI)

• Perspectives from the Front: Bench to Bedside (biomarker discovery highlighting NIH role - difficulties of integration of different data types and protocols)

• Discovery and Dissolving Barriers between Clinical Care and Research (Early examples)

• SNPs in Healthcare (many new ones proposed and tested -but do not address the multi-gene nature of many diseases and epigenetic problems…..)



Year in Review of Translational Year in Review of Translational

Bioinformatics Russ Altman, Stanford Univ.Bioinformatics Russ Altman, Stanford Univ.

• Used PubMed and Google Scholar searches to find top papers - of

which 27 were reported by Altman

• High throughput sequence analysis and genome-wide association

studies related to diseases now ubiquitous (eg Wellcome Trust

Consortium study reported in Nature covering 14,000 cases of 7

common diseases); significant neuroscience studies of brain cell

populations; Tylenol effect on liver from blood gene expression

signatures; validation issues in microarray studies of cancer outcomes;

gene expression predicting malarial response categories; etc.

• Main Informatics papers on heterogeneous dataset integration; text

mining, OMIM shows disease-linked genes have more physical

interaction; ontologies and citations on Medline; etc.

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Genomics ProteomicsEpidemiology

Environmental Medicine

Clinical Practice, Research, Education

Bioinformatics

Medical/Health/Consumer Informatics

Biophysics/engineeringBiophysics/engineering

Controlled Clinical TrialsControlled Clinical Trials

Public Health Informatics

Spectrum of Biomedical Informatics for Spectrum of Biomedical Informatics for

Translational Healthcare: Micro to MacroTranslational Healthcare: Micro to Macro

Systems BiologySystems Biology



Diversity of Biomedical Informatics:Diversity of Biomedical Informatics:

Subjects, Contents, GoalsSubjects, Contents, Goals

Individual Patients or Subjects

Health records and related documentsHealth records and related documents

Health care, clinical research, education

Populations of patients, hosts & parasites, microorganisms

Health and environment records, documents, mapsHealth and environment records, documents, maps

Public health, epidemiological research, education

Slide 15

Biomolecules, genomes, cells, tissues, organisms, microbiomes

Experimental designs and records of resultsExperimental designs and records of results

Biosciences research, biomedical systems R & DBiosciences research, biomedical systems R & D

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Slide 3

Goals within the Translational Spectrum are very Goals within the Translational Spectrum are very

differentdifferent

. . Medical goals emphasize Medical goals emphasize personal decisions:personal decisions:

diagnosis, prognosis and treatment of diagnosis, prognosis and treatment of individual individual

patientspatients;;

. Biomedical Research focuses on . Biomedical Research focuses on generalizable generalizable

experimentation for discovery: evidenceexperimentation for discovery: evidence of patterns of patterns

over groups of phenotypes and genotypes at various over groups of phenotypes and genotypes at various

levels (from the molecule to organisms and their levels (from the molecule to organisms and their

populations).populations).



Genomics Proteomics Epidemiology


Clinical Practice, Research

and Education

Bioinformatics Medical/Health/Consumer Informatics


Mechanism & HypothesisMechanism & Hypothesis--driven driven

Experimentation & DiscoveryExperimentation & Discovery

Adaptation to Individual CareAdaptation to Individual Care

Stratified Group/Population Stratified Group/Population

Exploratory and HypothesisExploratory and Hypothesis--driven driven




Translational Biomedical Informatics: Translational Biomedical Informatics:

Contrast of Research & PracticeContrast of Research & Practice

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Genomics Proteomics Epidemiology


Clinical Practice, Research

and Education

Bioinformatics

Medical/Health/Consumer Informatics


Mechanism & HypothesisMechanism & Hypothesis--driven driven


Adaptation to Individual CareAdaptation to Individual Care

Stratified Group/Population Stratified Group/Population

Exploration and HypothesisExploration and Hypothesis--driven driven




Biomedical Informatics: Biomedical Informatics:

Contrast of Research & PracticeContrast of Research & Practice

Medical Medical

Expertise & Expertise &

JudgmentJudgment

Individual caseIndividual case--basedbased

Health Care PracticeHealth Care Practice



Genomics

Proteomics

Epidemiology

Environmental MedicineClinical Practice, Research

and EducationBioinformatics

Medical & Health Informatics

Evidence and Personalization in

Biomedical Informatics


MechanismMechanism--based Experimentationbased Experimentation

NEW SCIENTIFIC BRIDGING PARADIGM NEW SCIENTIFIC BRIDGING PARADIGM

For Adaptation to Individual CareFor Adaptation to Individual Care

PopulationPopulation--based Experimentationbased Experimentation


??

Individual caseIndividual case--basedbased


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Genomics

Proteomics

Epidemiology

Environmental MedicineClinical Practice, Research

and EducationBioinformatics

Medical & Health Informatics

Evidence and Personalization in

Biomedical Informatics


MechanismMechanism--based Experimentationbased Experimentation

NEW SCIENTIFIC BRIDGING PARADIGM NEW SCIENTIFIC BRIDGING PARADIGM

For Adaptation to Individual Care:For Adaptation to Individual Care:

PopulationPopulation--based Experimentationbased Experimentation


MathematicalMathematical--StatisticalStatistical--CognitiveCognitive

Informatics that is Computationally ScalableInformatics that is Computationally Scalable

and and IndividualizableIndividualizable

Individual CaseIndividual Case--basedbased




Central Translational Medicine

Challenge is INFORMATICS

• Massive combinatorial search and selection for

trusted sources, studies, methods and content.

• Filtering for applicability to the individual,

reliability of source information, and interactions

of content into a patient-specific information

(PSI) model.

• Application of the PSI model in the context of

risk, cost, and uncertainty of the patient evidence

and environment.

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Key is the Integration of knowledge sources Key is the Integration of knowledge sources

for biomedical inquiry and how to apply to for biomedical inquiry and how to apply to

the individual patientthe individual patient

• Reasoning strategies for discovery, testing, and evaluating hypotheses (relations between formal and cognitive models - what is bioconsensus?)

• Knowledge representations for biomedicine (ontologies, biomathematical models, simulation, operations research, artificial intelligence, and experimental design)

• Representations of the biomedical literature (text mining, modeling/logic of arguments, annotation of images, diagramatic abstraction and reasoning)



Key Key

Translational Translational

Challenge:Challenge:

Personalized Personalized

vsvs. .

EvidenceEvidence--

BasedBased

MedicineMedicine

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Computational vs. Theoretical Computational vs. Theoretical

AspectsAspects

• Computational scalability, while difficult,

has always been overcome up to now by

new technology and theory…….

• Adequacy of theories for “translating”

general mathematical-statistical-cognitive

models from mechanism/group evidence to

the individual remains a deep, open problem

in (computational) epistemology……



Today: Incremental Today: Incremental

Translational InformaticsTranslational Informatics. Clinical and epidemiological inference based on

existing models - how and when to extract how and when to extract information from scientific models and textsinformation from scientific models and texts and heuristic/systematicheuristic/systematic application to individuals and population subgroups (Computational Vision and Deep Blue Chess strategic choice analogies for starting the process?)

. How to develop clinical and translational casedevelop clinical and translational case--based based applicability guidelines more scientificallyapplicability guidelines more scientifically – what are different models of evidence applied to decision-making and problem-solving for different environmental, organizational, and social constraints and their individual applicability…..

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Beyond Incremental Translational Beyond Incremental Translational

InformaticsInformatics

. Relation between formal and informal models of decision

making under risk and uncertainty: rational choice/game

theory and its alternatives or, visual cognition for exploring vs.

formal models for testing hypotheses….

. Bioethics models and informatics - research, practice and

education

. Models of Biomedical and Health Care Organizations



SynergiesSynergies betweenbetween BioinformaticsBioinformatics & & MedicalMedical

InformaticsInformatics[adapted from Maojo and Kulikowski, BIOINFOMED 02]

Expertise in clinical

systems and practice applications

Informatics tools and methods

Informatics modelling methodology and practice

Theoretical grounding in molecular and biophysical

systems and population genetics models

Biological Foundations(Molecular/

cellular)

(Tissue/organ/

system)

Medical InformaticsBioinformatics

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Interdisciplinary Workshop Example

from Genetic EpidemiologyDIMACS Workshop on

Computational Issues in Genetic Epidemiology August 21 - 22, 2008

DIMACS Center, CoRE Building, Rutgers University

Organizers: Andrew Scott Allen, Duke University, andrew.s.allen at duke.edu ,Ion Mandoiu, University of Connecticut, ion at engr.uconn.edu ,Dan Nicolae, University of Chicago, nicolae at galton.uchicago.edu, Yi Pan, Georgia State University, pan at cs.gsu.edu,Alex Zelikovsky, Georgia State

University,alexz at cs.gsu.edu

Workshop Announcement: There is strong evidence that genes play a major role in susceptibility to all

common human diseases. While linkage analysis has been very successful in finding the genes involved

in Mendelian diseases such as Huntington disease, early onset Alzheimer's disease and cystic fibrosis,

current interest has shifted towards mapping genes involved in diseases with complex etiologies such

as diabetes and cancer, for which association studies have been shown to be more powerful.

The workshop will bring together computer scientists,geneticists, and statisticians aiming to address

current computational challenges in gene mapping, which include dealing with complex missing data

patterns, multiple hypotheses testing, population substructure,gene-gene and gene-environment

interactions. New directions of research, such as capturing the effects of structural genomic

variation and using biological networks in whole-genome studies, will also be investigated.



Educational Challenge from the Complexity Educational Challenge from the Complexity

and Heterogeneity of Knowledge required for and Heterogeneity of Knowledge required for

Translational ResearchTranslational Research

Greatest challenges are of understanding our genotypes - phenotype relationships and how they interact with the environment - what are our extended phenotypes and can we develop a true systeomics that extend to the psychological and social dimensions

Documents

Medical Informatics and Bioinformatics in …Biomedical Informatics Laboratory STBIO Panel Discussions - Snapshot of Translational Biomedical Informatics • Informatics for Genome-Phenome