GeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes | Zendy

Yi-Hsiung Chen | Zendy; Chi-Dung Yang | Zendy; ChingPing Tseng | Zendy; HsienDa Huang | Zendy; ShinnYing Ho | Zendy

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GeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes

Author(s) -

Yi-Hsiung Chen,

Chi-Dung Yang,

ChingPing Tseng,

HsienDa Huang,

ShinnYing Ho

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv075

Subject(s) - computer science , data mining , gene regulatory network , stability (learning theory) , machine learning , gene , biology , gene expression , biochemistry

The establishment of quantitative gene regulatory networks (qGRNs) through existing network component analysis (NCA) approaches suffers from shortcomings such as usage limitations of problem constraints and the instability of inferred qGRNs. The proposed GeNOSA framework uses a global optimization algorithm (OptNCA) to cope with the stringent limitations of NCA approaches in large-scale qGRNs.

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