TY - JOUR
T1 - Identification of master regulator candidates in conjunction with network screening and inference
AU - Saito, Shigeru
AU - Zhou, Xinrong
AU - Bae, Taejeong
AU - Kim, Sunghoon
AU - Horimoto, Katsuhisa
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84883528688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883528688&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2013.056077
DO - 10.1504/IJDMB.2013.056077
M3 - Article
C2 - 24417028
AN - SCOPUS:84883528688
VL - 8
SP - 366
EP - 380
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
SN - 1748-5673
IS - 3
ER -