Classification of Splice Junction DNA sequence through Data mining techniques

机译：通过数据挖掘技术对剪接点DNA序列进行分类

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Data mining on DNA sequences is gaining immense importance in state-of-the-art research as researchers and clinicians are placing more emphasis on detecting genetic markers for disease prediction and inventing new drugs for therapeutic purpose. This paper highlights the role played by machine learning algorithms in classifying a given splice gene sequence into three classes (Intron-Exon, ExonIntron, neither) that clearly differentiate between the DNA that is needed for protein creation and the superfluous DNA that is removed during protein generation. This research work involves the execution of nine classification algorithms on the Splice junctions of 3190 DNA sequences taken from the Keel data repository, each having 60 nucleotides, to detect the boundaries between introns and exons that will further aid in the process of analyzing genetic markers and understanding the mechanism of protein synthesis. The Quinlan's C4.5 algorithm and the Random Tree classification algorithm reveal 99.97% classifier accuracy on this dataset. The validity of the results has been verified by classification of test data sets using the crafted classification framework. This work will enable accurate prediction of splice junctions in a DNA sequence whose class label is unknown.

机译：随着研究人员和临床医生将更多的精力放在检测用于疾病预测的遗传标记和发明用于治疗目的的新药物上，DNA序列的数据挖掘在最新研究中正变得越来越重要。本文重点介绍了机器学习算法在将给定的剪接基因序列分为三类（Intron-Exon，ExonIntron，均未区分）中所起的作用，这三类明确区分了蛋白质创建所需的DNA和蛋白质生成过程中去除的多余DNA。代。这项研究工作涉及对从Keel数据存储库中提取的3190个DNA序列的剪接点执行九种分类算法，每个序列具有60个核苷酸，以检测内含子和外显子之间的边界，这将进一步有助于分析遗传标记和了解蛋白质合成的机制。 Quinlan的C4.5算法和随机树分类算法在此数据集上显示99.97％的分类器准确性。通过使用精心设计的分类框架对测试数据集进行分类，已验证了结果的有效性。这项工作将能够准确预测其类别标签未知的DNA序列中的剪接点。

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《2012 international conference on future communication and computer technology》|2012年|143-148|共6页
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作者
Shomona Gracia Jacob; R.Geetha Ramani; P.Nancy;
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作者单位

Research Scholar, Dept. of Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, Tamilnadu, India;

Professor Head, Dept. of Computer Science and Engineering, Rajalakshmi Engineering College,Thandalam, Chennai, Tamilnadu, India;

Research Scholar, Dept. of Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, Tamilnadu, India;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
Data mining; Classification; splice gene sequence; Clinical data;

机译：数据挖掘;分类;剪接基因序列;临床数据;

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1. Fuzzy splicing in precursor-mRNA sequences: prediction of aberrant splice-junctions in viral DNA context [J] . Perambur S. Neelakanta, Sharmistha Chatterjee, Mirjana Pavlovic, Journal of Biomedical Science and Engineering . 2011,第4期

机译：前体mRNA序列中的模糊剪接：病毒DNA上下文中异常剪接连接的预测。
2. Delineation of Techniques to Implement on the Enhanced Proposed Model Using Data Mining for Protein Sequence Classification [J] . Ananya Basu, Suprativ Saha International Journal of Database Management Systems . 2014,第1期

机译：使用数据挖掘进行蛋白质序列分类的增强提议模型实施技术的描述
3. A Fast Contiguous Sequential Pattern Mining Technique in DNA Data Sequences Using Position Information [J] . Syeda Farzana Zerin, Byeong-Soo Jeong IETE Technical Review . 2011,第6期

机译：DNA数据序列中使用位置信息的快速连续序列模式挖掘技术
4. Classification of Splice Junction DNA sequence through Data mining techniques [C] . Shomona Gracia Jacob, R.Geetha Ramani, P.Nancy International conference on future communication and computer technology . 2012

机译：通过数据挖掘技术进行接头结DNA序列的分类
5. Mining techniques for data streams and sequences. [D] . Chu, Fang. 2005

机译：数据流和序列的挖掘技术。
6. Classification of Skin Disease using Ensemble Data Mining Techniques [O] . Anurag Kumar Verma, Saurabh Pal, Surjeet Kumar 2019

机译：使用集成数据挖掘技术对皮肤疾病进行分类
7. Three novel types of splicing aberrations in the tuberous sclerosis TSC2 gene caused by mutations apart from splice consensus sequences11Accession numbers and URLs for data in this article are as follows: Online Mendelian Inheritance in Man: http://www.ncbi.nlm.nih.gov/omim. For TSC1 (MIM 191100) and TSC2 (MIM 191092). The Human Gene Mutation Data Base, Cardiff (HGMD): http://www.uwcm.acuk/uwcm/mg. For TSC1 120735 and for TSC2 120466. TSC Variation Database: http://www.expmed.bwh.harvard.edu/projects/tsc_database. GenBank: http://www.ncbi.nlm.nih.gov/Genbank. For TSC2 cDNA X75621 and TSC2 complete genomic sequence AC005600. [O] . Mayer Karin, Ballhausen Wolfgang, Leistner Werner, 2000

机译：除剪接共有序列外，由突变引起的结节性硬化性TSC2基因中的三种新型剪接畸变11本文的登录号和URL如下：Man的在线孟德尔遗传：http：//www.ncbi.nlm.nih。 gov / omim。用于TSC1（MIM 191100）和TSC2（MIM 191092）。人类基因突变数据库，加的夫（HGMD）：http：//www.uwcm.acuk/uwcm/mg。对于TSC1 120735和TSC2120466。TSC变更数据库：http://www.expmed.bwh.harvard.edu/projects/tsc_database。 GenBank：http：//www.ncbi.nlm.nih.gov/Genbank。对于TSC2 cDNA X75621和TSC2，完整的基因组序列AC005600。
8. Splice Junction Classification Problems for DNA Sequences: Representation Issues. [R] . Sarkar, M., Leong, T. 2001

机译：DNa序列的剪接连接分类问题：表示问题。

Classification of Splice Junction DNA sequence through Data mining techniques

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