Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response

We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ("motifs") in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ("parents"). Thus our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. Rather than focusing on the regression task of predicting real-valued gene expression measurements, GeneClass performs the classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond ...

• Type: Conference
• Year: 2004
• Where: RECOMB
• Authors: Manuel Middendorf, Anshul Kundaje, Chris Wiggins, Yoav Freund, Christina S. Leslie
• Keywords: Computational Biology, Genetic Regulatory Networks, Genome-wide CDNA Microarray, Motif Profile Data, RECOMB 2004