Machine learning in genetics and genomics

By Maxwell W. Libbrecht, William Stafford Noble. Nature reviews genetics, 2015

1,776 citations recorded by the source corpus.

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9873d43696165b50fab955d27b9dde838c0a0152

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Citations in the corpus

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iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, methodology): “In recent years, machine learning techniques have been increasingly used as a powerful means to predict structural and functional properties of proteins and to assist in the annotation of genomic and proteomic data (Larranaga et al., 2006; Libbrecht and Noble, 2015).”
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Machine learning and computer vision approaches for phenotypic profiling (2017, background): “In contrast, classification is a form of supervised learning in which models are trained on labeled datasets to generate predictions on unseen data points (Libbrecht and Noble, 2015).”