A neural network based algorithm for gene expression prediction from chromatin structure

Gene expression is a very complex process, which is finely regulated and modulated at different levels. The first step of gene expression, the transcription of DNA into mRNA, is in turn regulated both at the genetic and epigenetic level. In particular, the latter, which involves the structure formed by DNA wrapped around histones (chromatin), has been recently shown to be a key factor, with post-translational modifications of histones acting combinatorially to activate or block transcription. In this work we addressed the problem of predicting the level of expression of genes starting from genome-wide maps of chromatin structure, that is, of the localization of several different histone modifications, which have been recently made available through the introduction of technologies like ChIP-Seq. We formalized the problem as a multi-class bipartite ranking problem, in which for each class a gene can be under-or over-expressed with respect to a given reference expression value. In order to deal with this problem, we exploit and extend a semi-supervised method (COSNet) based on a family of Hopfield neural networks. Benchmark genome-wide tests performed on six different human cell lines yielded satisfactory results, with clear improvements over the alternative approach most commonly adopted in the literature.