Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.

IF 2.6 4区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
3 Biotech Pub Date : 2024-11-01 Epub Date: 2024-10-09 DOI:10.1007/s13205-024-04107-2
Apoorva, Vikas Handa, Shalini Batra, Vinay Arora
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引用次数: 0

Abstract

This study investigates the ability to predict DNA methylation patterns in cervical cancer cells using decision-tree-based ensemble approaches and neural network-based models. The research findings suggest that a model based on random forest achieves a significant prediction accuracy of 91.35%. This projection was derived from comprehensive experimentation and a meticulous performance evaluation of the random forest model, employing a range of measures including Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, F1-score, Recall, and Precision. The results indicate that the random forest model exhibits superior performance compared to other tree-based models such as the Simple Decision Tree and XGBoost, as well as neural network-based models including Convolutional Neural Networks, Feed Forward Networks, and Wavelet Neural Networks. The findings indicate that using random forest-based techniques has great potential for future study and might be highly valuable in clinical applications, especially in improving diagnostic and treatment strategies based on epigenetic profiles.

推进宫颈癌的表观遗传学分析:对 DNA 甲基化模式进行分类的机器学习技术。
本研究调查了使用基于决策树的集合方法和基于神经网络的模型预测宫颈癌细胞中 DNA 甲基化模式的能力。研究结果表明,基于随机森林的模型的预测准确率高达 91.35%。这一预测结果来自对随机森林模型的全面实验和细致的性能评估,采用了一系列衡量标准,包括准确度、灵敏度、特异度、马修斯相关系数、F1-分数、召回率和精确度。结果表明,与其他基于树的模型(如简单决策树和 XGBoost)以及基于神经网络的模型(包括卷积神经网络、前馈网络和小波神经网络)相比,随机森林模型表现出更优越的性能。研究结果表明,使用基于随机森林的技术具有巨大的研究潜力,在临床应用中可能极具价值,特别是在改进基于表观遗传特征的诊断和治疗策略方面。
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来源期刊
3 Biotech
3 Biotech Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
6.00
自引率
0.00%
发文量
314
期刊介绍: 3 Biotech publishes the results of the latest research related to the study and application of biotechnology to: - Medicine and Biomedical Sciences - Agriculture - The Environment The focus on these three technology sectors recognizes that complete Biotechnology applications often require a combination of techniques. 3 Biotech not only presents the latest developments in biotechnology but also addresses the problems and benefits of integrating a variety of techniques for a particular application. 3 Biotech will appeal to scientists and engineers in both academia and industry focused on the safe and efficient application of Biotechnology to Medicine, Agriculture and the Environment.
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