MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction.

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.

Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes.

Motivation: The identification of cell cycle-regulated genes through the cyclicity of messenger RNAs in genome-wide studies is a difficult task due to the presence of internal and external noise in microarray data. Moreover, the analysis is also complicated by the loss of synchrony occurring in cell cycle experiments, which often results in additional background noise.

Results: To overcome these problems, here we propose the LEON (LEarning and OptimizatioN) algorithm, able to characterize the 'cyclicity degree' of a gene expression time profile using a two-step cascade procedure.

A convergent hybrid decomposition algorithm model for SVM training.

Training of support vector machines (SVMs) requires to solve a linearly constrained convex quadratic problem. In real applications, the number of training data may be very huge and the Hessian matrix cannot be stored. In order to take into account this issue, a common strategy consists in using decomposition algorithms which at each iteration operate only on a small subset of variables, usually referred to as the working set.