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Advances, obstacles, and opportunities for machine learning in proteomics
dc.contributor.author | Desaire, Heather | |
dc.contributor.author | Go, Eden P. | |
dc.contributor.author | Hua, David | |
dc.date.accessioned | 2023-02-13T17:40:17Z | |
dc.date.available | 2023-02-13T17:40:17Z | |
dc.date.issued | 2022-10-19 | |
dc.identifier.citation | Desaire, H., Go, E. P., & Hua, D. (2022). Advances, obstacles, and opportunities for machine learning in proteomics. Cell reports. Physical science, 3(10), 101069. https://doi.org/10.1016/j.xcrp.2022.101069 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/33785 | |
dc.description.abstract | The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated. | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2022 The Author(s). This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). | en_US |
dc.rights.uri | https:// creativecommons.org/licenses/by-nc-nd/4.0 | en_US |
dc.subject | Proteomics | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Supervised classification | en_US |
dc.subject | Biomarker | en_US |
dc.subject | AI | en_US |
dc.subject | Mass spectrometry | en_US |
dc.title | Advances, obstacles, and opportunities for machine learning in proteomics | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Desaire, Heather | |
kusw.kuauthor | Go, Eden P. | |
kusw.kuauthor | Hua, David | |
kusw.kudepartment | Chemistry | en_US |
dc.identifier.doi | 10.1016/j.xcrp.2022.101069 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-2181-0112 | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC9648337 | en_US |
dc.rights.accessrights | openAccess | en_US |