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Next-Generation Machine Learning for Biological Networks.
Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J.
Afiliação
  • Camacho DM; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • Collins KM; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Powers RK; Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
  • Costello JC; Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address: james.costello@ucdenver.edu.
  • Collins JJ; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, M
Cell ; 173(7): 1581-1592, 2018 06 14.
Article em En | MEDLINE | ID: mdl-29887378
ABSTRACT
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / Aprendizado de Máquina Idioma: En Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / Aprendizado de Máquina Idioma: En Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos