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The Classification of Gene Sequencer Based on Machine Learning

Published: 28 March 2022 Publication History

Abstract

Abstract: Biological sequencing plays a very important role in life science, especially with the improvement of sequencing technology and the development of sequencing instruments, and a large number of biological sequencing quality data are produced every day. Because of different sequencers, the quality of sequencing is different. In the process of sequencing quality control, the model of sequencer can be deduced according to the quality of gene sequence. Therefore, in this paper, five sequencers of Illumina HiSeq series, Illumina HiSeq 2000, Illumina HiSeq 2500, Illumina HiSeq 3000, Illumina HiSeq 4000 and Illumina HiSeq XTen, are selected as the classification objects. Firstly, the sequencing quality data of the five sequencers are preprocessed. Then, the classification model is trained by three machine learning algorithms: decision tree, logistic regression and support vector machine. The experimental results show that the accuracy rates of the three machine learning algorithms are 96.67%, 97.50% and 97.50% respectively. These algorithms are very good to solve the problem of using biological sequencing data quality to classify sequencer.

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EBIMCS '21: Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science
December 2021
539 pages
ISBN:9781450395687
DOI:10.1145/3511716
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 28 March 2022

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  1. Classification of gene Sequencer
  2. Machine learning
  3. Quality of sequencing

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EBIMCS 2021

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Overall Acceptance Rate 143 of 708 submissions, 20%

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