Panel: The Broader Role of Artificial Intelligence in Large-Scale Scientific Research

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Panel The Broader Role of Artificial
Intelligence in Large-Scale Scientific Research

• Joel Saltz MD, PhD
• Professor Biomedical Informatics, Computer
  Science
• Davis Chair in Cancer Research
• The Ohio State University
• Visiting Professor ISI, USC

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Translational Research
Disease mechanism, disease classification,
diagnosis, treatment

• Biology, biotechnology bioinformatics

Credit NIH SPORE Guidelines
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Imaging, Medical Analysis and Grid Environments
(IMAGE)September 16 - 18 2003

• Identify, query, retrieve, carry out on-demand
  data product generation directed at collections
  of data from multiple sites/groups on a given
  topic, reproduce each groups data analysis and
  carry out new analyses on all datasets. Should be
  able to carry out entirely new analyses or to
  incrementally modify other scientists data
  analyses. Should not have to worry about physical
  location of data or processing.

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caBIG 50 Grid Enabled Cancer Centers
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Biomedical Research Grids Types of Information

• Radiological Studies
• Pathology
• Molecular (Proteomics, gene expression)
• Genetic, Epigenetic (SNPs, haplotype analysis)
• Laboratory, pharmacy, outcome data

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Example Some Data types generated by OSU
Comprehensive Cancer Center
. .
Shared Resource Example data types
Molecular Cytogenetics Datasets from Karyotype analysis, data from SKY/FISH experiments
Analytical Cytometry FACSCaliber output, raw data from DiVaOption system,
Genotyping and Sequencing DNA sequence information for the sample, primer sequence, PCR sequencing information
Microarray Output from Affymetrix gene expression and Custom microarray analyses
Mouse Phenotyping Digitized radiographic, gross, and histologic images, hematology characterization
Real Time-PCR Description of sample plate, raw and processed output files
Tissue Procurement Anonymized pathology report, age, gender, race, tissue procurement id, patient id (if consent form is available), consent form, virtual slide of the tissue, if available
Leukemia Tissue Bank Sample processing date, date sample taken from the patient, accession id, patient id, patient name and last name, specimen type, number of tubes, diagnosis info, protocol
Proteomics 1D and 2D gel images, sample information (PI name, analysis method, instrument name), diagnosis, protein expressions for spots
Clinical Trials Office Protocol descriptions, investigator, title, status, approval processing ids, clinical data for patients on protocols, lab reports, adverse events, and trial outcomes.
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caBIG Problem Statement

• Production of data outstripping our ability to
  analyze it
• The research community may not be aware of other
  work and datasets
• Researchers may not tag data with the definition
  of the data they produce
• Semantic information is not often encoded nor
  included with data sets
• Data Islands or Silos of information are
  produced based on the problems outlined above
• A small group of knowledgeable people transmit
  data amongst themselves
• Modern exploratory research requires the
  integration of disparate databases of biological
  information to explain results
• To elucidate the mechanism behind disease we must
  aggregate data from many databases

Peter Corbitz NCI
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Vision

• Provide the Grid For Cancer Research so that we
  may
• Raise awareness of disparate datasets in the
  biological research community
• Allow research groups to exchange datasets with
  ease
• Allow research groups to understand the semantics
  of the datasets that they publish without always
  having to get on the phone
• Allow for quicker publication of the analysis of
  integrated data

• Blind Man/Elephant problem high throughput
  techniques, molecular imaging are powerful but
  each contribute only a piece to a puzzle

Peter Corbitz - NCI
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Bleeding Edge Pilot Project
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Example Ohio State BISTI Center for Grid Enabled
Image Analysis Novartis Molecular Imaging Studies
(Knopp)
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Example Genotype Phenotype Correlation

• Genetic, phenotypic data related via phylogentic
  tree
• 3473 SNPs among 11 strains of inbred mice
• Tree represents ancestry relationship between
  strains
• C3HHEJ and DBA2J are mutations associated with
  high heart rate variability
• Once candidate genotypes are identified, gather
  additional information about candidates from
  wrapped data sources
• Integration of Gene-Drug relationships in cancer
  treatment
• (Janies, Knoblock, Khan, Saltz)

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Example Classification of Neuroblastoma

• Decision-tree models current classification
  system
• Close collaboration with leading Pathologist
  (Shimada USC) who developed classifications
• Automate analysis, correlate with molecular,
  outcome data
• Classification determines treatment
• Childrens Oncology Group
• Scope North America, Australia, New Zealand

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Example Use Case from Abramson Cancer Center

• Use Case
• A research would like to study the error rate in
  pathological diagnoses of solid tumor samples
  and compare numerous molecular diagnostic
  approaches to determine if the molecular
  diagnostic approach can enhance the accuracy of
  pathological diagnoses.
• Query
• I want all solid tumors, specifically for lung
  cancer, that have a diagnosis based on tumor
  pathology. Each diagnosis must have an image of
  the tumor that allows for independent
  verification of diagnoses. Each record retrieved
  must also have either proteomics marker data or
  microarray data (Affy or two-color) included so
  that different molecular techniques can be
  correlated to the tumor pathology. In addition, I
  want all protein annotations for markers and
  genes associated with the proteomics and
  microarray data so I can perform meta-analyses.

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Issues Biomedical Grid Architecture

• What metadata needs to be described
• How to enforce standardization and completeness
  of metadata description
• Is it practical for everyone to use the same
  ontologies?
• If not, how to handle local variations in
  controlled manner
• Are there middleware solutions that can help
  (yes!)
• Data grid techniques for query, management of
  very large grid based datasets
• Role for immutable gridbased datatypes?

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Issues Biomedical Grid Architecture

• Distributed Ontology Management
• Many sites, many types of complex data
• Sites need to have freedom to create local
  ontology variants in controlled manner
• Systematic methods for controlled management,
  query of ontology variants
• Heuristic datatyping
• Data quality control
• Well defined structure (e.g. XML schema)
  sanity checks to check accuracy and
  completeness of metadata
• e.g. are data values consistent with what would
  be expected in an affymetrix gene expression
  dataset?

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Issues In Silico Research - Not Your Fathers
Datamining

• Example Predict clinical outcome
• Goal optimize function F that predicts outcome
  by combining clinical, molecular, image data
• Molecular, image data in turn need to be
  interpreted and analyzed
• Need to find image analysis functions Gi and
  molecular data analysis functions Hi that make it
  possible best predict outcome
• Functions Gi and Hi make use of domain specific
  knowledge (e.g. phylogentic trees, histologic
  classifications, pathways)

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Issues Incorporating ad-hoc data sources
Information Integration

• Not all data sources will post precise metadata
  definitions, ontologies etc
• Biomedical researchers should be able to use
  non-conforming data
• Goal is to develop a system where we automate the
  integration of data sources that are easy and
  natural
• Wrap datasources, define metadata, ontologies
  that allow data integration
• Middleware to cache ad-hoc data and to make
  ad-hoc information a first class grid citizen

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Issues Security Requirements

• Patients can give consent to for some data for
  some studies or classes of studies
• Researchers need to be able to control access to
  data
• IRBs can approve release of identified data to
  some individuals
• IRBs can specify how deidentification is to be
  carried out and when deidentified data can be
  released
• Cooperative study may have different IRB-dictated
  constraints at different sites
• Individuals associated with a given study may
  have different roles with different data access
  permissions
• Access requests and successful accesses must be
  logged

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Issues Security Requirements

• Need ontology based description of roles,
  ownership, permissions
• Validate correctness of description
• Conditions should lead to expected results (i.e.
  if one specifies rules, does one end up with
  counter-intuitive results)

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Thanks!
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Mobius

• Middleware system that provides support for
  management of metadata definitions (defined as
  XML schemas) and efficient storage and retrieval
  of data instances in a distributed environment.
• Mechanism for data driven applications to cache,
  share, and asynchronously communicate data in a
  distributed environment
• Grid based distributed, searchable, and shareable
  persistent storage
• Infrastructure for grid coordination language

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Global Model Exchange

• Store and link data models defined inside
  namespaces in grid.
• Enables other services to publish, retrieve,
  discover, remove, and version metadata
  definitions
• Services composed in a DNS-like architecture
  representing parent-child namespace hierarchy
• When a schema is registered in GME, it is stored
  in under the name and name space specified by the
  application schema is assigned a version number

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Finding candidate genes

• Complex, multi-factorial diseases
• CAD, diabetes, schizophrenia
• Long candidate lists
• Variations between individuals in their response
  to treatments
• Non-responders to statin treatment
• GOAL Link phenotypes to genotypes

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Genotype ltgt Phenotype

• APPROACH establish QTLs by correlating changes
  in genotype with changes in phenotype
• Genotype SNPs
• Use in mapping Haplotype blocks
• Functional come in many flavors cis-regulatory,
  non-synonymous, mRNA stability
• Phenotypes Mice
• Inbred strains provide well characterized
  genotypes
• Publicly available quantitative phenotype data
  (http//www.jax.org/phenome/)

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BISP Demonstration Mouse Phenotype Trait Query
Mouse Phenotype/SNP Analysis
Distributed Data Storage
Mako
Mako
Mako
processing
Distributed query
Mobius
Virtual Mako Service
internet
SNP IDs
Trait query
trait
HUGO IDs, BLAST Alignments
CERF Service Delivery System
CERF Server
MouseTraitQueryResult
(resource instance)
Action ViewGoMiner
ActionView
CERF Client
GOMiner
HTML Browser
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BRTT Demonstrations June 21, 2000 Mouse
SNP-Trait Association Demonstration
Execute query
A CERF Action/Service binding allows the query to
be executed - user prompted for the name of
a Trait (HDL6).
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Image Data Processing
Digitized Microscopy
DCE-MRI Studies
Visualization of Terabyte Scale, Multiresolution
Data
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Image Analysis Middleware Framework

• The Distributed Metadata and Data Management
  service to keep track of workflows, image
  datasets, and analysis results.
• Manage metadata associated with images, analysis
  results, annotations in a distributed
  environment.
• Federate databases of image, clinical, and
  molecular data in a distributed environment
• The Image Data Storage Service to manage the
  storage resources on the server and encapsulates
  efficient storage methods for image datasets.
• Create and maintain large-scale, on-line
  databases of images (from gigabytes to multiple
  terabytes in size) on disk-based storage
  clusters.
• The Distributed Execution Service to support
  on-demand analysis of image data
• Execute simple and complex image analysis
  operations and workflows on distributed
  collections of images.
• Integrate data retrieval and processing on
  commodity clusters and multiprocessor machines

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BRTT Demonstrations June 21, 2000 Rat Placenta
Microscopy Image Demonstration
Search for Images of Interest
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Prototype based on caCORE

• cancer Common Ontologic Representation
  Environment (caCORE)
• caCORE is the technology stack that facilitates
  data integration across multiple scientific
  disciplines

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caGRID Core architecture
caGRID Extension (Integration of Discovery and
Query Services)
Client
OGSA-DAI Globus
caGRID extension (Concept Discovery)
caGRID extension (Federated Query)
OGSA-DAI
caGRID extension (metadata)
caGRID extension (query)
Grid
Globus
Strongly typed XML transport, Metadata
Management (Mobius)
Data Source
caBIO server
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Petabyte sized rotating storage archives are no
longer hypothetical
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Ohio Supercomputing Center Mass Storage Testbed

• 50 TB of performance storage
• home directories, project storage space, and
  long-term frequently accessed files.
• 420 TB of performance/capacity storage
• Active Disk Cache - compute jobs that require
  directly connected storage
• parallel file systems, and scratch space.
• Large temporary holding area
• 128 TB tape library
• Backups and long-term "offline" storage

IBMs Storage Tank technology combined with TFN
connections will allow large data sets to be
seamlessly moved throughout the state with
increased redundancy and seamless delivery.