| Supervised inference of gene regulatory networks from positive and unlabeled examples. | |
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MedLine Citation:
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PMID: 23192540 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell's working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples. |
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Authors:
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Fantine Mordelet; Jean-Philippe Vert |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Methods in molecular biology (Clifton, N.J.) Volume: 939 ISSN: 1940-6029 ISO Abbreviation: Methods Mol. Biol. Publication Date: 2013 |
Date Detail:
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Created Date: 2012-11-29 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9214969 Medline TA: Methods Mol Biol Country: United States |
Other Details:
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Languages: eng Pagination: 47-58 Citation Subset: IM |
Affiliation:
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Department of Computer Science, Duke University, Durham, NC, USA, fantinemordelet@gmail.com. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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