DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma.

all rights reserved Genetic algorithms (Gas) have been successfully applied to many difficult search and optimisation problems in a diversity of research domains, including chemometrics and near infrared (nIr) spectroscopy. the application of Gas in chemometrics has previously been reviewed by riccardo leardi. 1,2 Ga applications in regression problems of chemometrics, molecular modelling, and various other applications related to chemistry are discussed in the first review. 1 the second review 2 is mainly a general introduction to Gas with chemistryrelated examples, but a few applications are also reviewed. leardi has also written a book on Gas and artificial neural networks (anns) in chemometrics. 3 Several fields related to chemometrics and chemistry, such as molecular modelling, design and recognition 4,5 and computation of protein folding, 6 have also benefited from tutorials and reviews of Gas. this review focuses on applications that include both Gas in methodology and spectroscopic data recorded in the nearinfrared range (Ga-nIr). In addition, some closely related areas are also partly covered. the section on variable and wavelength selection methods partly overlaps with leardi's review. 1 the emphasis is on: (a) how Gas have been performing relative to other optimisation methods, (b) the problem of over-fitting and (c) multi-criteria optimisation. the third section deals with multi-criteria optimisation and optimised pre-processing

Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences.Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.Contact: matthew.studham@scilifelab.seSupplementary information: Supplementary data are available at Bioinformatics online.