Next-Generation Machine Learning for Biological Networks

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.

Keywords

BiologyComputational biologyEvolutionary biology

MeSH Terms

AlgorithmsComputational BiologyDatabasesFactualDrug DiscoveryDrug-Related Side Effects and Adverse ReactionsHumansMachine LearningMicrobiotaNeural NetworksComputer

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Publication Info

Year: 2018
Type: review
Volume: 173
Issue: 7
Pages: 1581-1592
Citations: 902
Access: Closed

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APA Style

                            
                                
                                    Diogo M. Camacho, 
                                
                                    Katherine M. Collins, 
                                
                                    Rani K. Powers
                                
                                et al.
                            
                            (2018). 
                            Next-Generation Machine Learning for Biological Networks. 
                            Cell
                            , 173
                            (7)
                            , 1581-1592.
                            https://doi.org/10.1016/j.cell.2018.05.015
                        

Identifiers

DOI: 10.1016/j.cell.2018.05.015
PMID: 29887378

Data Quality

Data completeness: 90%