title: Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
abstract: Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.
authors.affiliation: Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. antti.honkela@hiit.fi
authors.forename: Antti
authors.initials: A
authors.lastname: Honkela
authors.forename: Magnus
authors.initials: M
authors.lastname: Rattray
authors.forename: Neil D
authors.initials: ND
authors.lastname: Lawrence
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D015894:Genome, Human; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D012333:RNA, Messenger; D014157:Transcription Factors; D016159:Tumor Suppressor Protein p53