Many forms of biomolecular (e.g., gene expression, genetics, proteomics) and clinical (e.g., clinical biomarkers, drug targets and indications) data pertaining to many different diseases are now readily available from publicly- available data repositories and knowledge-bases. There is now an opportunity to integrate these data into a unified, globally coherent representation of human disease, or nosology. Such a nosology would express how diseases are related to one another across multiple molecular and clinical axes. In this competitive renewal, we are planning a major expansion for this project. We plan to capture data from newer public repositories with more types of molecular measurements. Inclusion of genetic and protein measurements will enable a richer modeling of diseases and disease similarity, beyond mRNA measurements. To help link the molecular changes seen in disease to genetic differences, we plan to incorporate Expression Quantitative Trait Loci (eQTLs) into our disease models, built from simultaneous genetic and expression measurements. To expand the utility of our nosology in personalized medicine, we plan to incorporate more quantitative epidemiological measurements on disease, and to model transitions between disease states using probabilistic relational modeling. We will compare our nosology with the well-known ICD-10 as well as ICD-11, under development. We will develop novel visualization methods for the complex of edges and nodes seen in nosologies. We also plan to test our nosology in two Driving Biological Projects, in small cell lung cancer and immunology and disease, specifically yielding novel diagnostics and therapeutics ready for clinical trials.

Public Health Relevance

In this competitive renewal, building from 36 publications in the first funding period, we plan to create a new disease classification based on clinical, molecular, and epidemiological data and knowledge, and to use this classification to identify novel diagnostics and drugs for small cell lung cancer and immunological disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM079719-07
Application #
8451401
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Long, Rochelle M
Project Start
2006-09-30
Project End
2016-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
7
Fiscal Year
2013
Total Cost
$597,568
Indirect Cost
$219,499
Name
Stanford University
Department
Pediatrics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Paik, Hyojung; Heo, Hyoung-Sam; Ban, Hyo-jeong et al. (2014) Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions. J Transl Med 12:99
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Hsu, Irving; Chen, Rong; Ramesh, Aditya et al. (2013) Systematic identification of DNA variants associated with ultraviolet radiation using a novel Geographic-Wide Association Study (GeoWAS). BMC Med Genet 14:62
Khatri, Purvesh; Roedder, Silke; Kimura, Naoyuki et al. (2013) A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 210:2205-21
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Patel, Chirag J; Chen, Rong; Kodama, Keiichi et al. (2013) Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum Genet 132:495-508
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MacLean, Diana Lynn; Heer, Jeffrey (2013) Identifying medical terms in patient-authored text: a crowdsourcing-based approach. J Am Med Inform Assoc 20:1120-7

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