T-HOD: A literature-based candidate gene database for hypertension, obesity and diabetes

Hong Jie Dai, Johnny Chi Yang Wu, Richard Tzong Han Tsai, Wen Harn Pan, Wen Lian Hsu

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Researchers are finding it more and more difficult to follow the changing status of disease candidate genes due to the exponential increase in gene mapping studies. The Text-mined Hypertension, Obesity and Diabetes candidate gene database (T-HOD) is developed to help trace existing research on three kinds of cardiovascular diseases: hypertension, obesity and diabetes, with the last disease categorized into Type 1 and Type 2, by regularly and semiautomatically extracting HOD-related genes from newly published literature. Currently, there are 837, 835 and 821 candidate genes recorded in T-HOD for hypertension, obesity and diabetes, respectively. T-HOD employed the state-of-art text-mining technologies, including a gene/disease identification system and a disease-gene relation extraction system, which can be used to affirm the association of genes with three diseases and provide more evidence for further studies. The primary inputs of T-HOD are the three kinds of diseases, and the output is a list of disease-related genes that can be ranked based on their number of appearance, protein-protein interactions and single-nucleotide polymorphisms. Unlike manually constructed disease gene databases, the content of T-HOD is regularly updated by our text-mining system and verified by domain experts. The interface of T-HOD facilitates easy browsing for users and allows T-HOD curators to verify data efficiently. We believe that T-HOD can help life scientists in search for more disease candidate genes in a less time- and effort-consuming manner.

Original languageEnglish
Article numberbas061
JournalDatabase : the journal of biological databases and curation
Volume2013
DOIs
StatePublished - 2013

Fingerprint

Dive into the research topics of 'T-HOD: A literature-based candidate gene database for hypertension, obesity and diabetes'. Together they form a unique fingerprint.

Cite this