Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics

Multicentre investigation on electrically evoked compound action potential and stapedius reflex: how do these objective measures relate to implant programming parameters?

Objectives: The aims of this study were to collect data on electrically evoked compound action potential (eCAP) and electrically evoked stapedius reflex thresholds (eSRT) in HiResolutionTM cochlear implant (CI) users, and to explore the relationships between these objective measures and behavioural measures of comfort levels (M-levels). Methods: A prospective study on newly implanted subjects was designed. The eCAP was measured intraoperatively and at first fitting through neural response imaging (NRI), using the SoundWaveTM fitting software. The eSRT was measured intra-operatively by visual monitoring of the stapes, using both singleelectrode stimulation and speech bursts (four electrodes stimulated at the same time). Measures of M-levels were performed according to standard clinical practice and collected at first fitting, 3 and 6 months of CI use. Results: One hundred seventeen subjects from 14 centres, all implanted unilaterally with a HiResolution CII Bionic Ear\uae or HiRes 90K\uae, were included in the study. Speech burst stimulation elicited a significantly higher eSRT success rate than single-electrode stimulation, 84 vs. 64% respectively. The NRI success rate was 81% intra-operatively, significantly increasing to 96% after 6 months. Fitting guidelines were defined on the basis of a single NRI measurement. Correlations, analysis of variance, and multiple regression analysis were applied to generate a predictive model for the M-levels. Discussion: Useful insights were produced into the behaviour of objective measures according to time, electrode location, and fitting parameters. They may usefully assist in programming the CI when no reliable feedback is obtained through standard behavioural procedures