Md. Sarwar Kamal, S. Nimmy, Muhammad Iqbal Hossain, N. Dey, A. Ashour, V. Santhi
{"title":"ExSep:使用Neural Skyline Filter的外显子分离过程","authors":"Md. Sarwar Kamal, S. Nimmy, Muhammad Iqbal Hossain, N. Dey, A. Ashour, V. Santhi","doi":"10.1109/ICEEOT.2016.7755515","DOIUrl":null,"url":null,"abstract":"Exons and Introns are complimentary parts of DNA and RNA. Due to excessive data set in biological science, it is sometimes very expensive and costly to extract meaningful information from such data set. To accelerate efficient and faster exons separation an automated system designed under Neural Skyline Filter(NeuralSF) and Bloom filter. This development allows the comparative analysis on performances among NeuralSF, Bloom Filter and processing without filter. The outcome of the experiments and simulations shows that NeuralSF outperforms other processes in both the cases as number of exons finding and timing. This system may help to reduce the redundant data set from large number of collections. Apart from that it will enable to handle big biological data.","PeriodicalId":383674,"journal":{"name":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"ExSep: An exon separation process using Neural Skyline Filter\",\"authors\":\"Md. Sarwar Kamal, S. Nimmy, Muhammad Iqbal Hossain, N. Dey, A. Ashour, V. Santhi\",\"doi\":\"10.1109/ICEEOT.2016.7755515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Exons and Introns are complimentary parts of DNA and RNA. Due to excessive data set in biological science, it is sometimes very expensive and costly to extract meaningful information from such data set. To accelerate efficient and faster exons separation an automated system designed under Neural Skyline Filter(NeuralSF) and Bloom filter. This development allows the comparative analysis on performances among NeuralSF, Bloom Filter and processing without filter. The outcome of the experiments and simulations shows that NeuralSF outperforms other processes in both the cases as number of exons finding and timing. This system may help to reduce the redundant data set from large number of collections. Apart from that it will enable to handle big biological data.\",\"PeriodicalId\":383674,\"journal\":{\"name\":\"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEOT.2016.7755515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEOT.2016.7755515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ExSep: An exon separation process using Neural Skyline Filter
Exons and Introns are complimentary parts of DNA and RNA. Due to excessive data set in biological science, it is sometimes very expensive and costly to extract meaningful information from such data set. To accelerate efficient and faster exons separation an automated system designed under Neural Skyline Filter(NeuralSF) and Bloom filter. This development allows the comparative analysis on performances among NeuralSF, Bloom Filter and processing without filter. The outcome of the experiments and simulations shows that NeuralSF outperforms other processes in both the cases as number of exons finding and timing. This system may help to reduce the redundant data set from large number of collections. Apart from that it will enable to handle big biological data.