Discovering Typical Transcription-Factors Patterns in Gene Expression Levels of Mouse Embryonic Stem Cells by Instance-Based Classifiers
The development of high-throughput technology in genome sequencing provide a large amount of raw data to study the regulatory functions of transcription factors (TFs) on gene expression. It is possible to realize a classifier system in which the gene expression level, under a certain condition, is regarded as the response variable and features related to TFs are taken as predictive variables. In this paper we consider the families of Instance-Based (IB) classifiers, and in particular the Prototype exemplar learning classifier (PEL-C), because IB-classifiers can infer a mixture of representative instances, which can be used to discover the typical epigenetic patterns of transcription factors which explain the gene expression levels. We consider, as case study, the gene regulatory system in mouse embryonic stem cells (ESCs). Experimental results show IB-classifier systems can be effectively used for quantitative modelling of gene expression levels because more than 50% of variation in gene expression can be explained using binding signals of 12 TFs; moreover the PEL-C identifies nine typical patterns of transcription factors activation that provide new insights to understand the gene expression machinery of mouse ESCs.
KeywordsKnowledge Discovery Instance-Based Learning High-throughput Sequencing ChIP-Seq RNA-Seq
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