Predicting genetic regulatory response using classification.

Predicting genetic regulatory response using classification. Research Abstract Details 

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Predicting genetic regulatory response using classification. Abstract Text:

Manuel Middendorf,Anshul Kundaje,Chris Wiggins,Yoav Freund,Christina Leslie,

MOTIVATION: Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. RESULTS: 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 measurements 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. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. AVAILABILITY: The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass

Predicting genetic regulatory response using classification. Publishing Authors By Initials

M Middendorf,A Kundaje,C Wiggins,Y Freund,C Leslie,

For similar proteins: transcription factors research abstracts see: proteins: transcription factors research

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Predicting genetic regulatory response using classification. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Bioinformatics (Oxford, England)

VOLUME: 20 Suppl 1

Page Numbers: i232-40

Journal Abbreviation: Bioinformatics

ISSN: 1460-2059

DAY: 4

MONTH: Aug

YEAR: 2004

Predicting genetic regulatory response using classification. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 9808944

Predicting genetic regulatory response using classification. Keywords Mesh Terms:

KEYWORDS: Transcription Factors

MESH TERMS: genetics

Chemical & Substance for Abstract: Predicting genetic regulatory response using classification. Information

Substance Name: Transcription Factors

Registry Number: 0

Grant and Affiliation Information for Predicting genetic regulatory response using classification.

AFFILIATION: Department of Physics, Columbia University, NY, NY 10027, USA.

Country: England

AGENCY: United States NLM

GRANT: LM07276-02

ACRONYM: LM

MEDLINETA: Bioinformatics

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