Eight quick tips for biologically and medically informed machine learning.

Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge an...

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Main Authors: Luca Oneto, Davide Chicco
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1012711
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author Luca Oneto
Davide Chicco
author_facet Luca Oneto
Davide Chicco
author_sort Luca Oneto
collection DOAJ
description Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. To address this drawback, we present eight guidelines outlining best practices for employing informed machine learning methods in biomedical sciences. These quick tips offer recommendations on various aspects of informed machine learning analysis, aiming to assist researchers in generating more robust, explainable, and dependable results. Even if we originally crafted these eight simple suggestions for novices, we believe they are deemed relevant for expert computational researchers as well.
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institution Kabale University
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publisher Public Library of Science (PLoS)
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spelling doaj-art-76cc972901174a5a81b41de1022320c42025-01-17T05:30:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101271110.1371/journal.pcbi.1012711Eight quick tips for biologically and medically informed machine learning.Luca OnetoDavide ChiccoMachine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. To address this drawback, we present eight guidelines outlining best practices for employing informed machine learning methods in biomedical sciences. These quick tips offer recommendations on various aspects of informed machine learning analysis, aiming to assist researchers in generating more robust, explainable, and dependable results. Even if we originally crafted these eight simple suggestions for novices, we believe they are deemed relevant for expert computational researchers as well.https://doi.org/10.1371/journal.pcbi.1012711
spellingShingle Luca Oneto
Davide Chicco
Eight quick tips for biologically and medically informed machine learning.
PLoS Computational Biology
title Eight quick tips for biologically and medically informed machine learning.
title_full Eight quick tips for biologically and medically informed machine learning.
title_fullStr Eight quick tips for biologically and medically informed machine learning.
title_full_unstemmed Eight quick tips for biologically and medically informed machine learning.
title_short Eight quick tips for biologically and medically informed machine learning.
title_sort eight quick tips for biologically and medically informed machine learning
url https://doi.org/10.1371/journal.pcbi.1012711
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AT davidechicco eightquicktipsforbiologicallyandmedicallyinformedmachinelearning