Benchmarking network propagation methods for disease gene identification.

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets.

CausalR: extracting mechanistic sense from genome scale data.

Summary: Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape.

Predictors of placebo group decline in the Alzheimer's disease Assessment Scale-cognitive subscale (ADAS-Cog) in 24 week clinical trials of Alzheimer's disease.

One limitation of several recent 24 week Alzheimer's disease (AD) clinical trials was the lack of cognitive decline detected by the AD Assessment Scale-cognitive subscale (ADAS-cog) in the placebo groups, possibly obscuring true medication effects. Data from 733 individuals in the placebo arms of six AD clinical trials performed 1996-1997 were pooled to examine the relationship of clinical, demographic, and genetic characteristics with the 24 week change in ADAS-cog. Baseline cognitive and functional status and the screening-to-baseline change in ADAS-cog were the strongest independent predictors of the 24 week change in ADAS-cog.