TY - JOUR
T1 - Investigating noise tolerance in an efficient engine for inferring biological regulatory networks
AU - Komori, Asako
AU - Maki, Yukihiro
AU - Ono, Isao
AU - Okamoto, Masahiro
N1 - Funding Information:
This study was supported by Grant-in-Aid for Scienti¯c Research on Innovative Areas (Research in a proposed area), and by Synthetic Biology for the Comprehension of Biomolecular Networks from the Ministry of Education, Culture, Sports, Science, and Technology, Japan (No. 23119001 (M. Okamoto)).
Publisher Copyright:
© 2015 Imperial College Press.
PY - 2015/6/18
Y1 - 2015/6/18
N2 - Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods.
AB - Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods.
UR - http://www.scopus.com/inward/record.url?scp=84929509112&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929509112&partnerID=8YFLogxK
U2 - 10.1142/S0219720015410061
DO - 10.1142/S0219720015410061
M3 - Article
C2 - 25790786
AN - SCOPUS:84929509112
SN - 0219-7200
VL - 13
JO - Journal of bioinformatics and computational biology
JF - Journal of bioinformatics and computational biology
IS - 3
M1 - 1541006
ER -