DOMSCNet:一种深度学习模型,用于使用多层omics数据对胃癌进行分类.
Kasmika Borah1, Himanish Shekhar Das1, Ram Kaji Budhathoki2
1Department of Computer Science and Information Technology, Cotton University, Hem Baruah Rd, Panbazar, Guwahati, Kamrup Metropolitan district, Assam 781001, India.
Briefings in bioinformatics
|April 3, 2025
概括
这项研究引入了一种新的混合深度循环神经网络,DOMSCNet,用于使用多层omics数据进行胃癌分类. 该DOMSCNet模型有效地识别了关键特征,并优于癌症数据分析的现有方法.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 下一代测序 (NGS) 加快了生物医学研究,特别是了解癌症的分子基础.
- 对癌症分析复杂的,高维的多层omics数据对于诊断和治疗至关重要.
- 现有的计算方法难以处理多种omics数据和有效的特征提取以进行综合分析.
研究的目的:
- 开发一项强大的混合特征选择 (HFS) 技术,以从多层omics数据集中进行最佳特征检测.
- 提出一种基于深度循环神经网络的新型混合型模型,DOMSCNet,用于胃癌分类.
- 为了确保模型在不同的omics数据集中的通用性,并对其在外部数据集上的性能进行验证.
主要方法:
- 开发了一种混合特征选择 (HFS) 技术,包括SelectKBest-最大相关性最小冗余-Boruta (SMB).
- 一个新的混合深度循环神经网络模型,DOMSCNet,是为胃癌分类而设计的.
- 在DOMSCNet模型的训练和验证中,使用了四个多层omics数据集和八个外部数据集进行了训练和验证.
主要成果:
- 与其他HFS方法相比,建议的SMB HFS技术表现出优越的性能.
- 在多个omics数据集中,DOMSCNet模型在分类胃癌方面取得了更高的准确性.
- 在验证测试中,DOMSCNet的表现优于现有的和其他拟议的分类器.
结论:
- 开发的HFS技术有效地从复杂的omics数据中提取最佳特征.
- DOMSCNet为胃癌分类提供了一种强大而可泛化的深度学习方法.
- 这项研究推进了癌症研究中多层omics数据分析的计算方法.
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