1. Representing, Querying and Mining Knowledge about Autism Phenotypes
Amar K. Das, MD, PhD
Departments of Medicine and
of Psychiatry and Behavioral Sciences

2. Outline
Prior work
NDAR project
Phenologue project
Future directions

3. Represent findings and their links using structured knowledge
Hasler G,et al. Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry (2006)
Represent findings and their links using structured knowledge

4. 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)

5. 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

6. OMIM

7. dbGaP
Mailman, M.D. Nature Genetics (2007)

8. PhenoWiki

9. PhenoWiki

10. Current Approaches Lack of standardization Lack of organization
Lack of computability

11. NDAR Project Systematic review Extension to NIFSTD ontology
Rulebase development

12. 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)

13. Systematic Review Different terms Different cutoff scores
e.g., ‘age of first phrases’ and ‘age of onset of phrase speech’
Different cutoff scores
e.g., ‘delayed word’
Different definitions
e.g., ‘regression’
e.g., use of different instruments

14. 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

15. NDAR Codebook

16. Extension to NIFSTD

17. 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

18. Phenotype Definitions
Phenologue Project
Query
Database
Phenotype Definitions
New Associations
Catalog
Analysis

19. Axiomé Rule Management Tool
Rule paraphrasing
Rule elicitation
Rulebase visualization
Knowledge mining using rules
Hassanpour. S., et al. RuleML (2009)

20. 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

21. Semantic Similarity

22. Semantic Clustering
Use vector space model and k-means clustering

23. Semantic Clustering Found 17 phenotype clusters Example cluster
ConcludePositiveHistoryofRegression
ConcludeNegativeHistoryofRegression
ConcludeQuestionableHistoryofRegression1
ConcludePositiveHistoryofRegression2
ConcludePositiveHistoryofRegression1
ConcludeQuestionableHistoryofRegression2
ConcludeNoPhrases
ConcludePhrases

24. Text Mining
Hassanpour. S., et al.
ACM IHI (submitted)

25. Evaluation of Precision
Level of Semantics
Precision
Only rules
62%
Only ontology hierarchies
73%
Both rules and ontology hierarchies
76%

26. Future Directions
Develop rule management technologies to support grouping
Expand ontology to capture multi-scale representation of endophenotypes