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Artificial intelligence in cancer target identification and drug discovery
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review an...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090746/ https://www.ncbi.nlm.nih.gov/pubmed/35538061 http://dx.doi.org/10.1038/s41392-022-00994-0 |
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author | You, Yujie Lai, Xin Pan, Yi Zheng, Huiru Vera, Julio Liu, Suran Deng, Senyi Zhang, Le |
author_facet | You, Yujie Lai, Xin Pan, Yi Zheng, Huiru Vera, Julio Liu, Suran Deng, Senyi Zhang, Le |
author_sort | You, Yujie |
collection | PubMed |
description | Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates. |
format | Online Article Text |
id | pubmed-9090746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90907462022-05-12 Artificial intelligence in cancer target identification and drug discovery You, Yujie Lai, Xin Pan, Yi Zheng, Huiru Vera, Julio Liu, Suran Deng, Senyi Zhang, Le Signal Transduct Target Ther Review Article Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates. Nature Publishing Group UK 2022-05-10 /pmc/articles/PMC9090746/ /pubmed/35538061 http://dx.doi.org/10.1038/s41392-022-00994-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article You, Yujie Lai, Xin Pan, Yi Zheng, Huiru Vera, Julio Liu, Suran Deng, Senyi Zhang, Le Artificial intelligence in cancer target identification and drug discovery |
title | Artificial intelligence in cancer target identification and drug discovery |
title_full | Artificial intelligence in cancer target identification and drug discovery |
title_fullStr | Artificial intelligence in cancer target identification and drug discovery |
title_full_unstemmed | Artificial intelligence in cancer target identification and drug discovery |
title_short | Artificial intelligence in cancer target identification and drug discovery |
title_sort | artificial intelligence in cancer target identification and drug discovery |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090746/ https://www.ncbi.nlm.nih.gov/pubmed/35538061 http://dx.doi.org/10.1038/s41392-022-00994-0 |
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