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BMCBI
2006

A classification-based framework for predicting and analyzing gene regulatory response

8 years 3 months ago
A classification-based framework for predicting and analyzing gene regulatory response
Background: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem -- predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of...
Anshul Kundaje, Manuel Middendorf, Mihir Shah, Chr
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where BMCBI
Authors Anshul Kundaje, Manuel Middendorf, Mihir Shah, Chris Wiggins, Yoav Freund, Christina S. Leslie
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