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Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581010/ https://www.ncbi.nlm.nih.gov/pubmed/36303798 http://dx.doi.org/10.3389/fbinf.2021.746712 |
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author | Lecca, Paola |
author_facet | Lecca, Paola |
author_sort | Lecca, Paola |
collection | PubMed |
description | Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale. |
format | Online Article Text |
id | pubmed-9581010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95810102022-10-26 Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge Lecca, Paola Front Bioinform Bioinformatics Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale. Frontiers Media S.A. 2021-09-22 /pmc/articles/PMC9581010/ /pubmed/36303798 http://dx.doi.org/10.3389/fbinf.2021.746712 Text en Copyright © 2021 Lecca. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Lecca, Paola Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title | Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title_full | Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title_fullStr | Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title_full_unstemmed | Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title_short | Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge |
title_sort | machine learning for causal inference in biological networks: perspectives of this challenge |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581010/ https://www.ncbi.nlm.nih.gov/pubmed/36303798 http://dx.doi.org/10.3389/fbinf.2021.746712 |
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