What is DisSim?

DisSim is a web system for exploring similar diseases and discovering relationships among diseases and therapeutic chemicals (TCs). The four-layer architecture including DATABASE, ALGORITHM, TOOLS, and VIEW is described as the following figure.
(1) DATABASE layer. This layer stores Disease Ontology (DO), disease-related genes, functional associations between genes, and TCs of diseases. Among them, DO, disease-related genes, and functional associations between genes are used by ALGORITHM layer for calculating similarity between diseases.
(2) ALGORITHM layer. Five algorithms of measuring similarity between diseases have been implemented, which include Resnik, Lin, Wang, PSB, and SemFunSim .
(3) TOOL layer. Two tools including SimDisExplore and SimPDExplore have been provided for exploring the similarity score between diseases. SimDisExplore is used to explore similar diseases for an inputted disease term. In comparison, SimPDExplore calculates the similarity for a given pair of diseases. Both SimDisExplore and SimPDExplore provide the function for comparing TCs of diseases.
(4) VIEW layer. Webpages are provided for viewing the results. It shows the similarity of pair-wise diseases and the p-value of the similarity score. It also provides network visualization of relationships among TCs and a pair of similar diseases.

 

Who are we?

Our group is Laboratory of Biological Software Engineering, and we come from College of Bioinformatics Science and Technology, Harbin Medical University. My research area focuses on disease-related database integration and mining. Related publications are as following:
(1) Cheng L, Wang GH, Jie Li, Zhang TJ, Xu PG, Wang YD. (2013) SIDD: a semantically integrated database for a disease global view. PLoS One 8: e75504.
(2) Cheng L, Li J, Wang YD. (2014) SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association. PLOS ONE 9: e99415.
(3) Cheng L, Li J, Wang YD. (2015) Using Semantic Association to Extend and Infer Literature-oriented Relativity between Terms. IEEE/ACM Trans Comput Biol Bioinform 12(6):1219-26.
(4) Cheng L, Shi HB, Wang ZZ, Hu Y, Yang HX, Zhou C, Sun J, Zhou M. (2016) IntNetLncSim: An integrative network analysis method to infer human lncRNA functional similarity. PLOS ONE (Accept).
(5) Cheng L, Zhang S, Hu Y. (2016) BLAT2DOLite: an online system for identifying significant relationships between genetic sequences and diseases. Oncotarget (Accept).