A proteome-scale map of the human interactome network

TL;DR: This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta- analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expressionData sets.

TL;DR: The miRNet 2.0 as mentioned in this paper is an easy-to-use web-based platform designed to help elucidate microRNA functions by integrating users' data with existing knowledge via network-based visual analytics.

TL;DR: BioPlex 3.0 as discussed by the authors is a cell-line-specific interaction network that includes 118,162 interactions among 14,586 proteins in 293T cells and 5,522 immunoprecipitations in HCT116 cells.

TL;DR: A network-based methodology to identify efficacious drug combinations for specific diseases is proposed, and it is found that successful combinations tend to target separate neighbourhoods of the disease module in the human interactome.

TL;DR: It is shown that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites.

TL;DR: The protein kinase complement of the human genome is catalogued using public and proprietary genomic, complementary DNA, and expressed sequence tag sequences to provide a starting point for comprehensive analysis of protein phosphorylation in normal and disease states and a detailed view of the current state of human genome analysis through a focus on one large gene family.

TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.

TL;DR: This work has revealed the genomic landscapes of common forms of human cancer, which consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of "hills" (Genes altered infrequently).