title: Supervised inference of gene regulatory networks from positive and unlabeled examples.
abstract: Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell's working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples.
authors.affiliation: Department of Computer Science, Duke University, Durham, NC, USA. fantinemordelet@gmail.com
authors.forename: Fantine
authors.initials: F
authors.lastname: Mordelet
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Vert
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D014157:Transcription Factors
chemical_list: D014157:Transcription Factors