ImaGene: a convolutional neural network to quantify natural selection from genomic data

Background The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework....

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 20; no. Suppl 9; pp. 337 - 12
Main Authors:	Torada, Luis, Lorenzon, Lucrezia, Beddis, Alice, Isildak, Ulas, Pattini, Linda, Mathieson, Sara, Fumagalli, Matteo
Format:	Journal Article
Language:	English
Published:	London BioMed Central 22.11.2019 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Adaptation Algorithms Alleles Artificial neural networks Binding sites Bioinformatics Biological evolution Biomedical and Life Sciences Cable television broadcasting industry Color Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer simulation Continuity (mathematics) Convolutional neural networks Databases, Genetic Deep learning Demographics Evolution (Biology) Evolutionary biology Evolutionary genetics Gene mapping Genetic research Genetics, Population Genomes Genomics Genomics - methods Humans Image manipulation Learning strategies Life Sciences Machine learning Mapping Microarrays Molecular modelling Mutation Natural selection Neural networks Neural Networks, Computer Novels Pattern recognition Phenotype Phenotypes Pipelines Polygenic inheritance Population Population genetics Positive selection Selection, Genetic Signatures Simulation Software Supervised machine learning United States Supervised machine learning Convolutional neural networks Population genetics Natural selection
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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