Translating genomic research into clinical practice faces tremendous challenges due to significant gaps between the two domains. To fill this gap, we have developed the concept of an ontology fingerprint - a set of ontology terms overrepresented in the PubMed abstracts linked to a gene or a disease along with the terms'corresponding enrichment p-value, to characterize genes and diseases. We further have quantified the relationship between a gene and a disease by comparing the ontology fingerprints of each - the more similar the ontology fingerprint between a gene and a disease, the more likely the gene plays a role in the disease. Hypothesis: Our overarching hypothesis is that new insights into the molecular mechanisms of complex diseases and translation of such insights into clinical practice can be achieved by developing ontology fingerprints from biomedical ontology and PubMed to bridge the gap between clinical medicine and genomics. This global hypothesis will be first evaluated by analyzing the reversal of diabetes, hepatic steatosis and obesity by gastric bypass surgery (GBS).
Specific Aims : (1) test the hypothesis that a novel gene network can be constructed by comparing the ontology fingerprints of human genes derived from the biomedical literature and available biomedical ontologies. (2) test the hypothesis that the novel gene network and ontology fingerprints can be used to decipher the molecular mechanisms of the reversal of diabetes and hepatic steatosis following GBS. (3) deploy ontology fingerprint to bridge the gap between genome information and clinical medicine. Significance: These studies will link genomic information to clinical concepts and translate biomedical literature and genomic information into clinical practice by employing ontology fingerprints to decipher the interplay and reversal of obesity, diabetes and hepatic steatosis after GBS. Bridging the gap between genomics and medicine by ontology fingerprints will enhance the delivery of clinical care to patients.

Public Health Relevance

The proposed study can potentially elucidate the mechanisms involved in the reversal of diabetes, obesity and hepatic steatosis after GBS. Our method can be applied to other genomic information that is well defined in research papers, connecting this information to clinical disease to improve public health.

National Institute of Health (NIH)
National Library of Medicine (NLM)
High Priority, Short Term Project Award (R56)
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Special Emphasis Panel (ZLM1-ZH-C (01))
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Ye, Jane
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Medical University of South Carolina
Schools of Medicine
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Chung, Dongjun; Lawson, Andrew; Zheng, W Jim (2017) A statistical framework for biomedical literature mining. Stat Med 36:3461-3474
Sun, Jingchun; Zhu, Kevin; Zheng, W et al. (2015) A comparative study of disease genes and drug targets in the human protein interactome. BMC Bioinformatics 16 Suppl 5:S1
Chen, Guocai; Zhao, Jieyi; Cohen, Trevor et al. (2015) Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature. Database (Oxford) 2015:bav034
Tao, Cui; Sun, Jingchun; Zheng, W Jim et al. (2015) Colorectal cancer drug target prediction using ontology-based inference and network analysis. Database (Oxford) 2015:
Qin, Tingting; Matmati, Nabil; Tsoi, Lam C et al. (2014) Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network. Nucleic Acids Res 42:e138