A Bayesian regression approach to the inference of regulatory networks from gene expression data

• DOI: 10.1093/bioinformatics/bti487
• Publication Date: 2005-05-07T00:50:01Z
• Authors: S. Rogers; M. Girolami
• Keywords: Likelihood Functions; Models, Statistical; Models, Genetic; Gene Expression Profiling; 0206 medical engineering; Bayes Theorem; 02 engineering and technology; Gene Expression Regulation; Regression Analysis; Algorithms; Oligonucleotide Array Sequence Analysis; Signal Transduction; Transcription Factors

There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections.Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections.