title: Integrative random forest for gene regulatory network inference.
abstract: BACKGROUND
  Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal, we propose a novel algorithm named iRafNet: integrative random forest for gene regulatory network inference.
  
  
  RESULTS
  iRafNet is a flexible, unified integrative framework that allows information from heterogeneous data, such as protein-protein interactions, transcription factor (TF)-DNA-binding, gene knock-down, to be jointly considered for GRN inference. Using test data from the DREAM4 and DREAM5 challenges, we demonstrate that iRafNet outperforms the original random forest based network inference algorithm (GENIE3), and is highly comparable to the community learning approach. We apply iRafNet to construct GRN in Saccharomyces cerevisiae and demonstrate that it improves the performance in predicting TF-target gene regulations and provides additional functional insights to the predicted gene regulations.
  
  
  METHODS
  The R code of iRafNet implementation and a tutorial are available at: http://research.mssm.edu/tulab/software/irafnet.html
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Francesca
authors.initials: F
authors.lastname: Petralia
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Jialiang
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Zhidong
authors.initials: Z
authors.lastname: Tu
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors