Developing a method for predicting DNA nucleosomal sequences using deep learning.

IF 1.4 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Technology and Health Care Pub Date : 2025-03-01 Epub Date: 2024-11-20 DOI:10.1177/09287329241297900
Nizal Alshammry
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引用次数: 0

Abstract

BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.ObjectiveTo create a deep learning model that can identify nucleosomes from nucleotide sequences and to show that simpler models outperform more complicated ones in solving biological challenges.MethodsA classifier was created utilising deep learning and machine learning approaches. The final model consists of two convolutional layers, one max pooling layer, two fully connected layers, and a dropout regularisation layer. This structure was chosen on the basis of the 'less is frequently more' approach, which emphasises simple design without large hidden layers.ResultsExperimental results show that deep learning methods, specifically deep neural networks, outperform typical machine learning algorithms for recognising nucleosomes. The simplified network architecture proved suitable without the requirement for numerous hidden neurons, resulting in effective network performance.ConclusionThis study demonstrates that machine learning and other computational techniques may streamline and expedite the resolution of biological issues. The model helps identify nucleosomes and can be used in future research or labs. This study discusses the challenges of understanding and addressing simple biological problems with sophisticated computer technology and offers practical solutions for academic and economic sectors.

开发一种利用深度学习预测DNA核体序列的方法。
深度学习擅长处理原始数据,因为它可以自动提取和分类高级特征。尽管生物学在数据分析方面不太受欢迎,但结合计算机技术可以改善生物学研究。目的建立一个能够从核苷酸序列中识别核小体的深度学习模型,并证明简单模型在解决生物学挑战方面优于复杂模型。方法利用深度学习和机器学习方法建立分类器。最终的模型由两个卷积层、一个最大池化层、两个完全连接层和一个dropout正则化层组成。这种结构的选择基于“少即是多”的方法,强调简单的设计,没有大的隐藏层。结果实验结果表明,深度学习方法,特别是深度神经网络,在识别核小体方面优于典型的机器学习算法。简化后的网络结构不需要大量的隐藏神经元,从而获得了有效的网络性能。结论本研究表明,机器学习和其他计算技术可以简化和加快解决生物学问题。该模型有助于识别核小体,并可用于未来的研究或实验室。本研究讨论了用复杂的计算机技术理解和解决简单的生物学问题所面临的挑战,并为学术和经济部门提供了实用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Technology and Health Care
Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL
CiteScore
2.10
自引率
6.20%
发文量
282
审稿时长
>12 weeks
期刊介绍: Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).
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