Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid develo...

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Autor principal:	Xiuchun Lin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	genome variation machine learning genomic mutation prediction Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f192ddda10b14758aedfc6fb22590295
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spelling	oai:doaj.org-article:f192ddda10b14758aedfc6fb225902952021-12-01T01:38:52ZGenomic Variation Prediction: A Summary From Different Views2296-634X10.3389/fcell.2021.795883https://doaj.org/article/f192ddda10b14758aedfc6fb225902952021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcell.2021.795883/fullhttps://doaj.org/toc/2296-634XStructural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.Xiuchun LinFrontiers Media S.A.articlegenomevariationmachine learninggenomic mutationpredictionBiology (General)QH301-705.5ENFrontiers in Cell and Developmental Biology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genome variation machine learning genomic mutation prediction Biology (General) QH301-705.5
spellingShingle	genome variation machine learning genomic mutation prediction Biology (General) QH301-705.5 Xiuchun Lin Genomic Variation Prediction: A Summary From Different Views
description	Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.
format	article
author	Xiuchun Lin
author_facet	Xiuchun Lin
author_sort	Xiuchun Lin
title	Genomic Variation Prediction: A Summary From Different Views
title_short	Genomic Variation Prediction: A Summary From Different Views
title_full	Genomic Variation Prediction: A Summary From Different Views
title_fullStr	Genomic Variation Prediction: A Summary From Different Views
title_full_unstemmed	Genomic Variation Prediction: A Summary From Different Views
title_sort	genomic variation prediction: a summary from different views
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f192ddda10b14758aedfc6fb22590295
work_keys_str_mv	AT xiuchunlin genomicvariationpredictionasummaryfromdifferentviews
_version_	1718405996769968128

Genomic Variation Prediction: A Summary From Different Views

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