In biomedical research, enormous effort has been spent on determination of the genes(G) associated to diseases/Phenotype(D). Single gene alone does not contribute to several human diseases but may be caused based on multiple genetic variants found in complex interactions. In order to identify these causative genes involved in several diseases, GenDisBase is developed. GenDisBase database is an organised collection of curated human G2D associations, G2G interactions and D2D similarities constructed from three sources such as database of OMIM(morbid-map), HPRD and MimMiner respectively. According to their analysis, D2D similarity values in the range [0,0.3] are not informative, while for similarities in the range [0.6,1] showed significant functional similarity between corresponding diseases, our method set as 0.5 and filter out the disease similarity from 5080 disease. Our database contains only the HGNC approved genes/proteins.

User-defined search tool is designed with two important queries such as disease or gene/protein for the retrieval of gene association, similar disease and disease association, protein/gene interaction respectively

Dr. Suresh Subramani		Dr. Jeyakumar Natarajan
PhD Student		Associate Professor

This work has been carried out as a part of the Department of Information Technology (DIT) project entitled "Text Mining and Data Warehousing of Protein Kinases Relationships and Pathways" at Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India.