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Machine Learning and Integrative Analysis of Biomedical Big Data
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical an...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6410075/ https://www.ncbi.nlm.nih.gov/pubmed/30696086 http://dx.doi.org/10.3390/genes10020087 |
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author | Mirza, Bilal Wang, Wei Wang, Jie Choi, Howard Chung, Neo Christopher Ping, Peipei |
author_facet | Mirza, Bilal Wang, Wei Wang, Jie Choi, Howard Chung, Neo Christopher Ping, Peipei |
author_sort | Mirza, Bilal |
collection | PubMed |
description | Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues. |
format | Online Article Text |
id | pubmed-6410075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64100752019-03-26 Machine Learning and Integrative Analysis of Biomedical Big Data Mirza, Bilal Wang, Wei Wang, Jie Choi, Howard Chung, Neo Christopher Ping, Peipei Genes (Basel) Review Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues. MDPI 2019-01-28 /pmc/articles/PMC6410075/ /pubmed/30696086 http://dx.doi.org/10.3390/genes10020087 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Mirza, Bilal Wang, Wei Wang, Jie Choi, Howard Chung, Neo Christopher Ping, Peipei Machine Learning and Integrative Analysis of Biomedical Big Data |
title | Machine Learning and Integrative Analysis of Biomedical Big Data |
title_full | Machine Learning and Integrative Analysis of Biomedical Big Data |
title_fullStr | Machine Learning and Integrative Analysis of Biomedical Big Data |
title_full_unstemmed | Machine Learning and Integrative Analysis of Biomedical Big Data |
title_short | Machine Learning and Integrative Analysis of Biomedical Big Data |
title_sort | machine learning and integrative analysis of biomedical big data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6410075/ https://www.ncbi.nlm.nih.gov/pubmed/30696086 http://dx.doi.org/10.3390/genes10020087 |
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