Details

Autor(en) / Beteiligte

• Camacho, Diogo M.
• Collins, Katherine M.
• Powers, Rani K.
• Costello, James C.
• Collins, James J.

Titel

Next-Generation Machine Learning for Biological Networks

Ist Teil von

• Cell, 2018-06, Vol.173 (7), p.1581-1592

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2018

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• 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. Machine-learning approaches are essential for pulling information out of the vast datasets that are being collected across biology and biomedicine. This Review considers the opportunities and challenges at the intersection of network biology and data science.