Predicting Genetic Regulatory Response Using Classification

Details Predicting Genetic Regulatory Response Using Classification Predicting Genetic Regulatory Response Using Classification

2004-11-12

arxiv.org/abs/q-bio/0411028v1

Proceedings of the Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB 2004), Bioinformatics 2

Biology

Quantitative Biology

Quantitative Methods

8 pages, 4 figures, presented at Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB 2004), su

Scientific paper

We present a novel classification-based method 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. We convert the regression task of predicting real-valued gene expression measurement to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S. cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks.

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