Investigating noise tolerance in an efficient engine for inferring biological regulatory networks.
J Bioinform Comput Biol
; 13(3): 1541006, 2015 Jun.
Article
in En
| MEDLINE
| ID: mdl-25790786
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Computational Biology
/
Systems Biology
Language:
En
Journal:
J Bioinform Comput Biol
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2015
Document type:
Article
Affiliation country:
Japan