PPInfer - Inferring functionally related proteins using protein interaction networks
Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions.
Last updated 5 months ago
softwarestatisticalmethodnetworkgraphandnetworkgenesetenrichmentnetworkenrichmentpathways
4.48 score 107 dependencies 1 dependentssurvtype - Subtype Identification with Survival Data
Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful.
Last updated 5 months ago
softwarestatisticalmethodgeneexpressionsurvivalclusteringsequencingcoverage
4.00 score 127 dependenciesgsean - Gene Set Enrichment Analysis with Networks
Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.
Last updated 5 months ago
softwarestatisticalmethodnetworkgraphandnetworkgenesetenrichmentgeneexpressionnetworkenrichmentpathwaysdifferentialexpression
4.00 score 108 dependencies