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. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes.

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. False positives are limited using the introduced Sequential Causal Analysis of Networks approach. AVAILABILITY AND IMPLEMENTATION: CausalR is implemented in R, parallelized, and is available from Bioconductor. CONTACT: glyn.x.bradley@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. The ADAS-cog did not detect progression in patients with mild dementia (screening Mini-Mental State Exam, MMSE, >or=20). The change in ADAS-cog from screening to baseline was inversely correlated with the 24 week change score; it was more difficult to detect cognitive decline at 24 weeks if individuals markedly worsened from screening to baseline. The effects of baseline MMSE and screening-to-baseline change in ADAS-cog generalized to the placebo group (N=106) of another AD study performed in 2004-2005. Overcoming lack of placebo decline in AD clinical trials will require scales more sensitive to cognitive decline in mild AD and strategies to reduce within-person variability in outcome measures.