Title: Identification of master regulator candidates in conjunction with network screening and inference

Authors: Shigeru Saito; Xinrong Zhou; Taejeong Bae; Sunghoon Kim; Katsuhisa Horimoto

Addresses: INFOCOM CORPORATION, Sumitomo Fudosan Harajuku Building, 2-34-17, Jingumae, Shibuya-ku, Tokyo 150-0001, Japan ' Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China ' Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Gwanak 599, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea ' Department of Molecular Medicine and Biopharmaceutical Sciences, and Medicinal Biocoveregence Research Centre, Seoul National University, Gwanak 599, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea ' Computational Biology Research Centre, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan

Abstract: We developed a procedure for identifying transcriptional Master Regulators (MRs) related to special biological phenomena, such as diseases, in conjunction with network screening and inference. Network screening is a system for detecting activated transcriptional regulatory networks under particular conditions, based on the estimation of graph structure consistency with the measured data. Since network screening utilises the known Transcriptional Factor (TF)-gene relationships as the experimental evidence for molecular relationships, its performance depends on the ensemble of known TF networks used for its analysis. To compensate for its restrictions, a network inference method, the path consistency algorithm, is concomitantly utilised to identify MRs. The performance is illustrated by means of the known MRs in brain tumours that were computationally inferred and experimentally verified. As a result, the present procedure worked well for identifying MRs, in comparison to the previous computational selection for experimental verification.

Keywords: gene regulatory networks; network screening; path consistency algorithm; brain tumours; transcriptional master regulators; bioinformatics; network inference; master regulator identification.

DOI: 10.1504/IJDMB.2013.056077

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.3, pp.366 - 380

Received: 02 May 2011
Accepted: 02 May 2011
Published online: 20 Oct 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article