rcNet: A Web Tool for Inferring Disease and Gene Set Association
rcNet (Rank Coherence in Networks) web tool provides an online resource to predict associations between disease phenotypes and gene sets. rcNet algorithms combine known disease-gene associations in OMIM with the topological information in the disease phenotype similarity network and the gene-gene interaction network to analyze the association between a gene set and disease phenotypes. The networks provide richer and more reliable information for computing the association scores used to rank the phenotypes. reNet algorithms could be applied to validate and analyze the candidate disease gene identified in GWAS, DNA copy number analysis, and Microarray gene expression profiling.
SVM-FOLD: A Web Tool for Protein Structural Classification.
This web server makes predictions of family, superfamily and fold level classifications of proteins based on the Structural Classification of Proteins (SCOP) hierarchy using the Support Vector Machine (SVM) learning algorithm.
SVM-FOLD detects subtle protein sequence similarities by learning from all available annotated proteins, as well as utilizing potential hits as identified by PSI-BLAST. Predictions of classes of proteins that do not have any known example with a significant pairwise PSI-BLAST E-value can still be found using SVMs.
- RankProp: A Web Tool for protein ranking in homology and remote homogy detection.
Rankprop is a ranking algorithm that exploits the global network structure of similarity relationships among proteins in a database by performing a diffusion operation on a protein similarity network with weighted edges.
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