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Effect of Alcohol on Brain Development. Normal. Fetal Alcohol Syndrome. Defining cognitive disorders by gene expression profiling: Examples of cognitive disabilities: Mental illness Mental retardation Alzheimer’s and other dementia Traumatic brain injury Stroke Fetal Alcohol Syndrome

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Presentation Transcript
slide1

Effect of Alcohol

on Brain Development

Normal

Fetal Alcohol

Syndrome

slide3

Defining cognitive disorders

  • by gene expression profiling:
  • Examples of cognitive disabilities:
  • Mental illness
  • Mental retardation
  • Alzheimer’s and other dementia
  • Traumatic brain injury
  • Stroke
  • Fetal Alcohol Syndrome
  • Gene profiling provides a
  • molecular fingerprint for susceptibility
  • and cause of specific cognitive disorders.
slide4

Microarray Expression Profiling for

Understanding Cognitive Disabilitites

  • Defining the human genome provides the database for
  • profiling gene expression patterns in cognitive disabilities.
  • Specific cognitive disabilities will not be a single disorder
  • but rather multiple disorders that manifest themselves
  • with a common medical diagnosis.
  • Gene array technology allows defining the spectrum of
  • gene expression profiles for “normal” individuals and those
  • with “specific” cognitive disorders.
  • Gene profiling allows “molecular fingerprinting” of an
  • individuals cognitive disorder.
the role of informatics in addressing cognitive deficits
The Role of Informatics in Addressing Cognitive Deficits
  • Neuroinformatics:
    • Integrating data from behavioral, physiological, anatomical, cellular and molecular levels
    • Building and testing neural models in silico
  • Molecular bioinformatics
    • Analysis of genes, gene expression arrays, etc.
    • Modelling metabolic and signalling pathways
  • General bioinformatics
    • Organizing, managing and extracting relevant information from the biological literature.
integrating diverse sources of data
Integrating Diverse Sources of Data
  • Relevant information is available from many sources, none designed to be interoperable
  • We have designed and implemented systems that use automated ontologies and advanced database technology to integrate diverse data from:
    • public and private databases
    • multiple institutions
    • multiple biological data types (e.g. QTLs & gene expression arrays)
finding patterns and relationships
Finding Patterns and Relationships
  • Machine learning: using data and computation to extend human intuition and statistical power
  • HSC bioinformatics invents new techniques, and has a powerful kit of existing tools:
    • Neural networks
    • Support vector machines
    • Information theory (e.g. mutual information)
    • Bayesian inference
predictive modelling in complex biological systems
Predictive Modelling in Complex Biological Systems
  • Biological systems are inherently non-linear; combinations and relationships are key
  • High-throughput revolution (e.g. gene expression arrays) creates enough data for pattern discovery
  • Sample applications:
    • Found a 4 gene combination that distinguishes among leukemias as well as 50 gene linear model
    • Combined 3 weak alcohol state markers into a near-perfect predictor from 1800 training examples.