Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes | Zendy

Marianna De Santis | Zendy; Francesco Rinaldi | Zendy; Emmanuela Falcone | Zendy; Stefano Lucidi | Zendy; Giulia Piaggio | Zendy; Aymone Gurtner | Zendy; Lorenzo Farina | Zendy

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Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes

Author(s) -

Marianna De Santis,

Francesco Rinaldi,

Emmanuela Falcone,

Stefano Lucidi,

Giulia Piaggio,

Aymone Gurtner,

Lorenzo Farina

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt671

Subject(s) - computational biology , human genome , computer science , genome , gene , artificial intelligence , machine learning , biology , genetics

The identification of cell cycle-regulated genes through the cyclicity of messenger RNAs in genome-wide studies is a difficult task due to the presence of internal and external noise in microarray data. Moreover, the analysis is also complicated by the loss of synchrony occurring in cell cycle experiments, which often results in additional background noise.

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