Cargando…
Omics data integration in computational biology viewed through the prism of machine learning paradigms
Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436311/ https://www.ncbi.nlm.nih.gov/pubmed/37600970 http://dx.doi.org/10.3389/fbinf.2023.1191961 |
_version_ | 1785092293982683136 |
---|---|
author | Fouché, Aziz Zinovyev, Andrei |
author_facet | Fouché, Aziz Zinovyev, Andrei |
author_sort | Fouché, Aziz |
collection | PubMed |
description | Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years. |
format | Online Article Text |
id | pubmed-10436311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104363112023-08-19 Omics data integration in computational biology viewed through the prism of machine learning paradigms Fouché, Aziz Zinovyev, Andrei Front Bioinform Bioinformatics Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years. Frontiers Media S.A. 2023-08-04 /pmc/articles/PMC10436311/ /pubmed/37600970 http://dx.doi.org/10.3389/fbinf.2023.1191961 Text en Copyright © 2023 Fouché and Zinovyev. 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 Fouché, Aziz Zinovyev, Andrei Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title | Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title_full | Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title_fullStr | Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title_full_unstemmed | Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title_short | Omics data integration in computational biology viewed through the prism of machine learning paradigms |
title_sort | omics data integration in computational biology viewed through the prism of machine learning paradigms |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436311/ https://www.ncbi.nlm.nih.gov/pubmed/37600970 http://dx.doi.org/10.3389/fbinf.2023.1191961 |
work_keys_str_mv | AT foucheaziz omicsdataintegrationincomputationalbiologyviewedthroughtheprismofmachinelearningparadigms AT zinovyevandrei omicsdataintegrationincomputationalbiologyviewedthroughtheprismofmachinelearningparadigms |