{"title":"基于TensorFlow的DNA序列错误校正","authors":"Hassanin M. Al-Barhamtoshy, R. Younis","doi":"10.1109/ACIT50332.2020.9300094","DOIUrl":null,"url":null,"abstract":"The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.","PeriodicalId":193891,"journal":{"name":"2020 21st International Arab Conference on Information Technology (ACIT)","volume":"339 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"DNA Sequence Error Corrections based on TensorFlow\",\"authors\":\"Hassanin M. Al-Barhamtoshy, R. Younis\",\"doi\":\"10.1109/ACIT50332.2020.9300094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.\",\"PeriodicalId\":193891,\"journal\":{\"name\":\"2020 21st International Arab Conference on Information Technology (ACIT)\",\"volume\":\"339 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 21st International Arab Conference on Information Technology (ACIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACIT50332.2020.9300094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 21st International Arab Conference on Information Technology (ACIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACIT50332.2020.9300094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DNA Sequence Error Corrections based on TensorFlow
The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.
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