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Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worl...

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Autores principales: Arjmand, Babak, Hamidpour, Shayesteh Kokabi, Tayanloo-Beik, Akram, Goodarzi, Parisa, Aghayan, Hamid Reza, Adibi, Hossein, Larijani, Bagher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8829119/
https://www.ncbi.nlm.nih.gov/pubmed/35154283
http://dx.doi.org/10.3389/fgene.2022.824451
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author Arjmand, Babak
Hamidpour, Shayesteh Kokabi
Tayanloo-Beik, Akram
Goodarzi, Parisa
Aghayan, Hamid Reza
Adibi, Hossein
Larijani, Bagher
author_facet Arjmand, Babak
Hamidpour, Shayesteh Kokabi
Tayanloo-Beik, Akram
Goodarzi, Parisa
Aghayan, Hamid Reza
Adibi, Hossein
Larijani, Bagher
author_sort Arjmand, Babak
collection PubMed
description Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.
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spelling pubmed-88291192022-02-11 Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer Arjmand, Babak Hamidpour, Shayesteh Kokabi Tayanloo-Beik, Akram Goodarzi, Parisa Aghayan, Hamid Reza Adibi, Hossein Larijani, Bagher Front Genet Genetics Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC8829119/ /pubmed/35154283 http://dx.doi.org/10.3389/fgene.2022.824451 Text en Copyright © 2022 Arjmand, Hamidpour, Tayanloo-Beik, Goodarzi, Aghayan, Adibi and Larijani. 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 Genetics
Arjmand, Babak
Hamidpour, Shayesteh Kokabi
Tayanloo-Beik, Akram
Goodarzi, Parisa
Aghayan, Hamid Reza
Adibi, Hossein
Larijani, Bagher
Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title_full Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title_fullStr Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title_full_unstemmed Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title_short Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
title_sort machine learning: a new prospect in multi-omics data analysis of cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8829119/
https://www.ncbi.nlm.nih.gov/pubmed/35154283
http://dx.doi.org/10.3389/fgene.2022.824451
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