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Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach
Nucleosides, Nucleotides & Nucleic Acids ( IF 1.3 ) Pub Date : 2020-06-22 , DOI: 10.1080/15257770.2020.1780440
Lopamudra Das 1 , Sarita Nanda 1 , J K Das 1
Affiliation  

Abstract Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As diseased genes are more random than the healthy ones, in this work, a comparison of the diseased gene is made against the healthy ones. An adaptive signal processing method like functional link artificial neural network-based Levenberg–Marquardt filter has been proposed in this regard. For parameter upgradation, the algorithm is modified using particle swarm optimization. Here, disease genes are discriminated from healthy ones based on the magnitude of mean square error (MSE), which is calculated through the adaptive filter. The performance of the algorithm is inspected by computing some evaluation parameters. Since accuracy is the prime concern, authors in this work have taken an attempt to improve the accuracy level compared to the existing methods. Taking the reference gene as healthy, the overall process is accomplished by categorizing the diseased and healthy targets with MSE value at a threshold of 0.012. The proposed technique predicts the test gene sets successfully.

中文翻译:

真核 DNA 中的遗传性疾病预测:一种自适应信号处理方法

摘要 使用信号处理方法对真核 DNA 进行遗传性疾病预测是生物信息学中一项令人难以置信的工作。各个领域的研究人员都试图提出一种非侵入性的方法来预测与疾病相关的基因。由于患病基因比健康基因更随机,因此在这项工作中,将患病基因与健康基因进行比较。在这方面已经提出了一种自适应信号处理方法,如基于功能链接人工神经网络的 Levenberg-Marquardt 滤波器。对于参数升级,使用粒子群优化修改算法。在这里,根据均方误差 (MSE) 的大小将疾病基因与健康基因区分开来,该大小是通过自适应滤波器计算出来的。通过计算一些评估参数来检查算法的性能。由于准确性是首要问题,因此与现有方法相比,这项工作的作者已尝试提高准确性水平。以参考基因为健康,整个过程是通过使用阈值为 0.012 的 MSE 值对患病和健康目标进行分类来完成的。所提出的技术成功地预测了测试基因集。
更新日期:2020-06-22
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