Cargando…
Machine learning: A powerful tool for gene function prediction in plants
Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past deca...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7394712/ https://www.ncbi.nlm.nih.gov/pubmed/32765975 http://dx.doi.org/10.1002/aps3.11376 |
_version_ | 1783565276214722560 |
---|---|
author | Mahood, Elizabeth H. Kruse, Lars H. Moghe, Gaurav D. |
author_facet | Mahood, Elizabeth H. Kruse, Lars H. Moghe, Gaurav D. |
author_sort | Mahood, Elizabeth H. |
collection | PubMed |
description | Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule‐based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning–based approaches in plants. |
format | Online Article Text |
id | pubmed-7394712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73947122020-08-05 Machine learning: A powerful tool for gene function prediction in plants Mahood, Elizabeth H. Kruse, Lars H. Moghe, Gaurav D. Appl Plant Sci Review Articles Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule‐based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning–based approaches in plants. John Wiley and Sons Inc. 2020-07-28 /pmc/articles/PMC7394712/ /pubmed/32765975 http://dx.doi.org/10.1002/aps3.11376 Text en © 2020 Mahood et al. Applications in Plant Sciences is published by Wiley Periodicals LLC on behalf of the Botanical Society of America This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Review Articles Mahood, Elizabeth H. Kruse, Lars H. Moghe, Gaurav D. Machine learning: A powerful tool for gene function prediction in plants |
title | Machine learning: A powerful tool for gene function prediction in plants |
title_full | Machine learning: A powerful tool for gene function prediction in plants |
title_fullStr | Machine learning: A powerful tool for gene function prediction in plants |
title_full_unstemmed | Machine learning: A powerful tool for gene function prediction in plants |
title_short | Machine learning: A powerful tool for gene function prediction in plants |
title_sort | machine learning: a powerful tool for gene function prediction in plants |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7394712/ https://www.ncbi.nlm.nih.gov/pubmed/32765975 http://dx.doi.org/10.1002/aps3.11376 |
work_keys_str_mv | AT mahoodelizabethh machinelearningapowerfultoolforgenefunctionpredictioninplants AT kruselarsh machinelearningapowerfultoolforgenefunctionpredictioninplants AT moghegauravd machinelearningapowerfultoolforgenefunctionpredictioninplants |