stanford center for biomedical informatics research representing, querying and mining knowledge...

Stanford Centerfor Biomedical Informatics Research

Representing, Querying and Mining Knowledge about Autism Phenotypes

Amar K. Das, MD, PhDDepartments of Medicine and

of Psychiatry and Behavioral Sciences

NDAR Ontology SIGJune 28, 2010

Outline Prior work NDAR project Phenologue project Future directions


Hasler G,et al. Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry (2006)

Represent findings and their links using structured knowledge


Phenomics

“A primary task for the new field of phenomics will be to clarify what, in practical terms, constitutes a phenotype and then to delineate the different phenotypic components that compose the phenome.”

Freimer & Sabatti, Nature Genetics (2003)


Phenotypes in Psychiatry

‘The observable structural and functional characteristics of an organism determined by its genotype and modulated by its environment’

Diagnostic component Intermediate phenotype Quantitative phenotype Covariates


OMIM


dbGaP

Mailman, M.D. Nature Genetics (2007)


PhenoWiki


Current Approaches Lack of standardization Lack of organization Lack of computability


NDAR Project Systematic review Extension to NIFSTD ontology Rulebase development


Systematic Review “(ADI-R or ADOS or Vineland) and

(genes or genetics) and autism” 26/43 papers relevant

156 unique phenotypes found Mean # phenotypes 4.1, range 0-13 Three basic types (1:1, sum, cutoff score)


Systematic Review Different terms

e.g., ‘age of first phrases’ and ‘age of onset of phrase speech’

Different cutoff scorese.g., ‘delayed word’

Different definitionse.g., ‘regression’e.g., use of different instruments


SWRL: Semantic Web Rule Language Rules in SWRL can be used to deduce

new knowledge about an existing OWL ontology

Specification can be extended through the use of built ins


NDAR Codebook


Extension to NIFSTD


Phenologue Project (R01 MH877)

Develop a knowledge base that maps phenotypes to brain connectivity, neural deficits, and genetic markers

Develop logic-based methods to encode and classify phenotypes based on multi-scale measurements

Create tools to acquire new phenotypes and annotate phenotype-genotype findings in online resources such as published literature

Develop query-elicitation methods that can evaluate hypotheses about the phenotypes using deductive inference


Phenologue Project

Database

Phenotype Definitions

New Associations

Query

Catalog Analysis


Axiomé Rule Management Tool Rule paraphrasing Rule elicitation Rulebase visualization Knowledge mining using rules

Hassanpour. S., et al. RuleML (2009)


Computational Phenomics Develop methods to

Apply machine learning methods to discover groups of rules with common semantics

Use natural language processing method to discover phenotype rules in published text


Semantic Similarity


Semantic Clustering Use vector space model and k-

means clustering


Semantic Clustering Found 17 phenotype clusters Example cluster

ConcludePositiveHistoryofRegressionConcludeNegativeHistoryofRegressionConcludeQuestionableHistoryofRegression1ConcludePositiveHistoryofRegression2ConcludePositiveHistoryofRegression1ConcludeQuestionableHistoryofRegression2ConcludeNoPhrasesConcludePhrasesConcludeNegativeHistoryofRegression


Text Mining

Hassanpour. S., et al. ACM IHI (submitted)


Evaluation of Precision

Level of Semantics Precision

Only rules 62%

Only ontology hierarchies 73%

Both rules and ontology hierarchies 76%


Future Directions Develop rule management

technologies to support grouping Expand ontology to capture multi-

scale representation of endophenotypes

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