The analysis of high-throughput data such as gene-expression assays usually results in a long list of """"""""significant genes."""""""" One commonly used method to gain insight into the biological significance of alterations in gene expression levels is to determine whether the Gene Ontology (GO) terms about specific biological processes, molecular functions, or cellular components are over- or under-represented in the annotations of the gene sets generated as the output of the statistical analysis. This analysis method often referred to as """"""""enrichment analysis,"""""""" can be used to summarize and profile a gene-set, as well as other genome scale data. While the GO has been the principal focus for enrichment analysis, we can carry out the same sort of profiling using any ontology available in the biomedical domain. We can perform enrichment analysis using disease ontologies - such as SNOMED-CT. For example, by annotating known protein mutations with disease terms, Mort et al. identified a class of diseases - blood coagulation disorders - that are associated with a significant depletion in substitutions a O-linked glycosylation sites. We can apply the enrichment analysis methodology to other datasets of interest - such as patient cohorts. For example, enrichment analysis might detect specific co-morbidities that have an increased incidence in rheumatoid arthritis patients - a topic of recent discussion in the literature and considered essential to provide high quality care. We can also ask translational questions;for example, by identifying other disease associations for the genes involved in a certain disease of interest we can gain insight into how the causation of seemingly unrelated diseases might be related, e.g., Werner's syndrome, Cockayne syndrome, Burkitt's lymphoma, and Rothmund-Thomson Syndrome are all related by the fact that they share the same underlying gene related to aging. Despite widespread adoption, GO-based enrichment analysis has intrinsic drawbacks. Our goal is to develop and apply general enrichment analysis methods - that can use any biomedical ontology - to profile diverse datasets, such as patient cohorts from electronic medical records and sets of genes deemed significant in genomic analyses. We propose to address some of the key shortcomings of the current enrichment-analysis methods, to expand significantly the ontologies that are used for such analyses, and to apply enrichment analysis on novel data sources for asking translational questions. The hypothesis spanning all our aims is that if we are successful, enrichment analysis - a widely used analysis approach by bioinformatics scientists - will be possible with more than just the GO and the method will be extended to ask clinical questions.

Public Health Relevance

If we are successful, enrichment analysis?a widely used analysis approach by bioinformatics scientists? will be possible with more than just the GO and the method will be extended to ask clinical questions. Our work is significant because we will extend the scope of enrichment analysis to the clinical domain. To the best of our knowledge, our work will the first to analyze a large corpus of millions of free-text clinical notes with ?omics? inspired, ontology-based methods to profile off-label usage and their associated safety profiles.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM011369-01A1
Application #
8495000
Study Section
Special Emphasis Panel (ZLM1-ZH-C (01))
Program Officer
Ye, Jane
Project Start
2013-09-01
Project End
2017-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
1
Fiscal Year
2013
Total Cost
$477,224
Indirect Cost
$157,667
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Yu, Yue; Chen, Jun; Li, Dingcheng et al. (2016) Systematic Analysis of Adverse Event Reports for Sex Differences in Adverse Drug Events. Sci Rep 6:24955
Jung, Kenneth; Covington, Scott; Sen, Chandan K et al. (2016) Rapid identification of slow healing wounds. Wound Repair Regen 24:181-8
Low, Yen Sia; Gallego, Blanca; Shah, Nigam Haresh (2016) Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records. J Comp Eff Res 5:179-92
Hripcsak, George; Ryan, Patrick B; Duke, Jon D et al. (2016) Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A 113:7329-36
Agarwal, Vibhu; Podchiyska, Tanya; Banda, Juan M et al. (2016) Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc :
Liu, Sijia; Liu, Hongfang; Chaudhary, Vipin et al. (2016) An Infinite Mixture Model for Coreference Resolution in Clinical Notes. AMIA Jt Summits Transl Sci Proc 2016:428-37
Agarwal, Vibhu; Shah, Nigam H (2016) LEARNING ATTRIBUTES OF DISEASE PROGRESSION FROM TRAJECTORIES OF SPARSE LAB VALUES. Pac Symp Biocomput 22:184-194
Poole, Sarah; Schroeder, Lee Frederick; Shah, Nigam (2016) An unsupervised learning method to identify reference intervals from a clinical database. J Biomed Inform 59:276-84
Winnenburg, Rainer; Shah, Nigam H (2016) Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature. BMC Bioinformatics 17:250
Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S et al. (2016) Predictive modeling of risk factors and complications of cataract surgery. Eur J Ophthalmol 26:328-37

Showing the most recent 10 out of 30 publications