基于拉马努金求和的数字FIR滤波器在真核基因编码区识别中的应用

Subhajit Kar, Madhabi Ganguly
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引用次数: 0

摘要

由于基因的多样性,寻找蛋白质编码区(即外显子)是一个复杂的问题。本文提出了一种基于拉马努金求和的FIR滤波方法,用于基因中蛋白质编码区的识别。在隐杆线虫(Caenorhabditis Elegans cosmid) f56f11 . a、GENSCAN、HMR195、ASP67、BG570等基准数据集上测试了所设计算法的有效性,并与基于antiinotch、Butterworth和Comb滤波器的成熟算法进行了比较。这里的生物序列的数值转换是一个整数序列,而拉马努金和总是生成一个整数的周期序列。这减少了量化误差,简化了硬件实现。设计的拉马努金求和控制滤波的评估是在外显子水平、核苷酸水平和通过ROC图进行的。F56F11.4基因特异性为82%,敏感性为97%,精密度为85%,ROC曲线AUC值为0.96平方单位。这些评价参数表明,与其他现有的外显子寻找技术相比,所提出的方法具有增强的结果。
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An efficient way of identification of protein coding regions of eukaryotic genes using digital FIR filter governed by Ramanujan's Sum
Finding protein coding regions, i.e., exons in a gene is a complex problem due to its diverse nature. In this paper, a novel FIR filtering governed by Ramanujan's Sum is proposed for identification of protein coding regions in gene. The efficacy of the designed algorithms is tested on Caenorhabditis Elegans cosmid F56F11.4a, various benchmark datasets like GENSCAN, HMR195, ASP67, and, BG570, and compared to well-established algorithms based on Antinotch, Butterworth, and Comb filters. The numerical conversion of the biological sequence here is an integer sequence and Ramanujan's Sum always generates a periodic sequence of integer numbers. This results in reduced quantisation error and simple hardware implementation. The evaluation of the designed Ramanujan's Sum governed filtering is done at the exonic level, nucleotide level, and through ROC plots. The results obtained on gene F56F11.4 attain specificity of 82%, sensitivity 97%, and precision of 85% while the AUC value of ROC curve was calculated as 0.96 square units. These evaluation parameters reveal that the proposed method gives enhanced results while comparing it to other existing exon-finding techniques.
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来源期刊
CiteScore
1.60
自引率
0.00%
发文量
73
期刊介绍: IJBET addresses cutting-edge research in the multi-disciplinary area of biomedical engineering and technology. Medical science incorporates scientific/technological advances combining to produce more accurate diagnoses, effective treatments with fewer side effects, and improved ability to prevent disease and provide superior-quality healthcare. A key field here is biomedical engineering/technology, offering a synthesis of physical, chemical, mathematical and computational sciences combined with engineering principles to enhance R&D in biology, medicine, behaviour, and health. Topics covered include Artificial organs Automated patient monitoring Advanced therapeutic and surgical devices Application of expert systems and AI to clinical decision making Biomaterials design Biomechanics of injury and wound healing Blood chemistry sensors Computer modelling of physiologic systems Design of optimal clinical laboratories Medical imaging systems Sports medicine.
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