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Transcript
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.