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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 •Gene profiling provides a molecular fingerprint for susceptibility and cause of specific cognitive disorders. 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 • 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 • 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 • 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 • 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 nearperfect predictor from 1800 training examples.