Next-generation machine learning for biological networks

• Diogo M. Camacho ^[1] ; Katherine M. Collins ^[2] ; Rani K. Powers ^[3]
  1. [1] Harvard University

     Harvard University

     City of Cambridge, Estados Unidos

     
  2. [2] Massachusetts Institute of Technology

     Massachusetts Institute of Technology

     City of Cambridge, Estados Unidos

     
  3. [3] University of Colorado Anschutz Medical Campus

     University of Colorado Anschutz Medical Campus

     Estados Unidos

     

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• Localización: Cell, ISSN 0092-8674, Vol. 173, Nº. 7, 2018, págs. 1581-1592
• Idioma: inglés
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• Resumen

  • 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.