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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Long, Rochelle M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code
Dewey, Frederick E; Grove, Megan E; Pan, Cuiping et al. (2014) Clinical interpretation and implications of whole-genome sequencing. JAMA 311:1035-45
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
Grossman, Adam D; Cohen, Mitchell J; Manley, Geoffrey T et al. (2013) Altering physiological networks using drugs: steps towards personalized physiology. BMC Med Genomics 6 Suppl 2:S7
Corona, Erik; Chen, Rong; Sikora, Martin et al. (2013) Analysis of the genetic basis of disease in the context of worldwide human relationships and migration. PLoS Genet 9:e1003447
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
Jahchan, Nadine S; Dudley, Joel T; Mazur, Pawel K et al. (2013) A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov 3:1364-77
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
Chen, B; Butte, A J (2013) Network medicine in disease analysis and therapeutics. Clin Pharmacol Ther 94:627-9
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

Showing the most recent 10 out of 53 publications