Bioinformatics (Oxford, England)

MotivationExploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treat…

MotivationIn light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases and drugs, few extensively …

MotivationComputational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, …

UNLABELLED: Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to …

SummaryWe created a deeply extracted and annotated database of genome-wide association studies (GWAS) results. GRASP v1.0 contains >6.2 million SNP-phenotype association from among 1390 GWAS studies. We re-annotated GWAS results with 16 ann…

MotivationMany techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease a…

UnlabelledDifferential dependency network (DDN) is a caBIG® (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biolog…