A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S.

Afiliación

Ni Y; Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Aghamirzaie D; Genetics, Bioinformatics and Computational Biology, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Elmarakeby H; Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Collakova E; Department of Plant Pathology, Physiology, and Weed Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Li S; Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Grene R; Department of Plant Pathology, Physiology, and Weed Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
Heath LS; Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.

Front Plant Sci ; 7: 1936, 2016.

Article en En | MEDLINE | ID: mdl-28066488

Palabras clave

Arabidopsis; binding site; cluster; gene expression; gene regulatory network; support vector machines

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Plant Sci Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos

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