Inferring Transcriptional Networks by Mining Omics Data

Title: Inferring Transcriptional Networks by Mining Omics Data

Volume: 1 Issue: 3

Author(s): Tim Van den Bulcke, Karen Lemmens, Yves Van de Peer and Kathleen Marchal

Affiliation:

Keywords: Module network, transcriptional network, network inference, systems biology

Abstract: Inferring comprehensive regulatory networks from high-throughput data is one of the foremost challenges of modern computational biology. As high-throughput expression profiling experiments have gained common ground in many laboratories, different techniques have been proposed to infer transcriptional regulatory networks from them. Furthermore, with the advent of diverse types of high-throughput data, the research in network inference has received a new impulse. The use of diverse types of data, together with the increasing tendency of building the inference on biologically plausible simplifications, allows a more reliable and more complete description of networks. Here, we discuss how the research focus in the field of network inference is increasingly shifting from methods trying to reconstruct networks from a single data type towards integrative approaches dealing with several data sources simultaneously to infer regulatory modules.

Cite this article as:

den Bulcke Tim Van, Lemmens Karen, Van de Peer Yves and Marchal Kathleen, Inferring Transcriptional Networks by Mining Omics Data, Current Bioinformatics 2006; 1 (3) . https://dx.doi.org/10.2174/157489306777827991