Paper Info
Title
Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes.
Abstract
Motivation: 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. Results: To overcome these problems, here we propose the LEON (LEarning and OptimizatioN) algorithm, able to characterize the 'cyclicity degree' of a gene expression time profile using a two-step cascade procedure. The first step identifies a potentially cyclic behavior by means of a Support Vector Machine trained with a reliable set of positive and negative examples. The second step selects those genes having peak timing consistency along two cell cycles by means of a non-linear optimization technique using radial basis functions. To prove the effectiveness of our combined approach, we use recently published human fibroblasts cell cycle data and, performing in vivo experiments, we demonstrate that our computational strategy is able not only to confirm well-known cell cycle-regulated genes, but also to predict not yet identified ones.
Year
DOI
Venue
2014
10.1093/bioinformatics/btt671
BIOINFORMATICS
Field
DocType
Volume
Genome,Data mining,Gene,Radial basis function,Background noise,Computer science,Microarray analysis techniques,Artificial intelligence,External noise,Support vector machine,Bioinformatics,Cell cycle,Machine learning
Journal
30
Issue
ISSN
Citations 
2
1367-4803
1
PageRank 
References 
Authors
0.37
5
7
Name
Order
Citations
PageRank
M. Santis1436.53
F. Rinaldi218119.61
Emmanuela Falcone310.37
Stefano Lucidi478578.11
Giulia Piaggio510.37
Aymone Gurtner610.37
Lorenzo Farina714636.14