SEBINI: Software Environment for BIological Network Inference

SEBINI: Software Environment for BIological Network Inference

UNLABELLED The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation...

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global app...

Strategies for increasing the applicability of biological network inference

Selective integration of multiple biological data for supervised network inference

MOTIVATION Inferring networks of proteins from biological data is a central issue of computational biology. Most network inference methods, including Bayesian networks, take unsupervised approaches in which the network is totally unknown in the beginning, and all the edges have to be predicted. A more realistic supervised framework, proposed recently, assumes that a substantial part of the netw...

Selective Integration of Multiple Genomic Data for Biological Network Inference

In the field of computational biology, recently there has been a surge of interest in biological networks such as protein interaction networks, gene regulatory networks, or metabolic networks, which help us to understand the cellular machinery. Most of biological networks represent the relationships between genes or proteins. Namely the existence of edges means that the corresponding genes/prot...