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Deep learning in non coding variant

The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequ...

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Main Authors: Xin, L. K., Abdullah, A.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/86557/1/AfnizanfaizalAbdullah2020_DeepLearninginNonCodingVariant.pdf
http://eprints.utm.my/id/eprint/86557/
https://dx.doi.org/10.11591/ijeecs.v18.i3.pp1432-1438
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spelling my.utm.865572020-09-30T08:41:41Z http://eprints.utm.my/id/eprint/86557/ Deep learning in non coding variant Xin, L. K. Abdullah, A. QA Mathematics The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequencing, genomics data had grown to become an ambiguous giant which could not keep up with the pace of its advancement in it analysis counter parts. This results in a large amount of unanalysed genomic data. These genomic data consist not only plain information, researcher had discovered the potential of most gene called the non-coding variant and still failing in identifying their function. With the growth in volume of data, there is also a growth of hardware or technologies. With current technologies, we were able to implement a more complex and sophisticated algorithm in analysis these genomics data. The domain of deep learning had become a major interest of researcher as it was proven to have achieve a significant success in deriving insight from various field. This paper aims to review the current trend of non-coding variant analysis using deep learning approach. Institute of Advanced Engineering and Science 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86557/1/AfnizanfaizalAbdullah2020_DeepLearninginNonCodingVariant.pdf Xin, L. K. and Abdullah, A. (2020) Deep learning in non coding variant. Indonesian Journal of Electrical Engineering and Computer Science, 18 (3). pp. 1432-1438. ISSN 2502-4752 https://dx.doi.org/10.11591/ijeecs.v18.i3.pp1432-1438 DOI:10.11591/ijeecs.v18.i3.pp1432-1438
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Xin, L. K.
Abdullah, A.
Deep learning in non coding variant
description The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequencing, genomics data had grown to become an ambiguous giant which could not keep up with the pace of its advancement in it analysis counter parts. This results in a large amount of unanalysed genomic data. These genomic data consist not only plain information, researcher had discovered the potential of most gene called the non-coding variant and still failing in identifying their function. With the growth in volume of data, there is also a growth of hardware or technologies. With current technologies, we were able to implement a more complex and sophisticated algorithm in analysis these genomics data. The domain of deep learning had become a major interest of researcher as it was proven to have achieve a significant success in deriving insight from various field. This paper aims to review the current trend of non-coding variant analysis using deep learning approach.
format Article
author Xin, L. K.
Abdullah, A.
author_facet Xin, L. K.
Abdullah, A.
author_sort Xin, L. K.
title Deep learning in non coding variant
title_short Deep learning in non coding variant
title_full Deep learning in non coding variant
title_fullStr Deep learning in non coding variant
title_full_unstemmed Deep learning in non coding variant
title_sort deep learning in non coding variant
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://eprints.utm.my/id/eprint/86557/1/AfnizanfaizalAbdullah2020_DeepLearninginNonCodingVariant.pdf
http://eprints.utm.my/id/eprint/86557/
https://dx.doi.org/10.11591/ijeecs.v18.i3.pp1432-1438
_version_ 1680321063577190400
score 13.211869

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