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预测lncRNA和疾病关联与图形自编码器和噪声强大的梯度增强噪声强大的梯度

Lili Tang¹, Liangliang Huang², Yi Yuan³

¹School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, China.

Scientific reports

|May 31, 2025

概括

这项研究介绍了LDA-GARB,这是一个用于预测长非编码RNA疾病关联 (LDA) 的新框架. 通过准确识别潜在的LDAs,LDA-GARB提高了疾病机制的理解和生物标志物的发现.

科学领域:

基因组学和生物信息学
计算生物学计算生物学
疾病机制研究疾病机制研究

背景情况:

长非编码RNA (lncRNAs) 在人类疾病中起着至关重要的作用.
准确识别 lncRNA-疾病关联 (LDAs) 对于理解疾病机制,发现生物标志物以及改善诊断和治疗至关重要.
现有的LDA预测方法需要改进,以提高准确性和稳定性.

研究的目的:

开发和验证一个新的计算框架,LDA-GARB,用于预测 lncRNA-疾病关联.
通过先进的特征提取和机器学习技术,提高LDA预测的准确性和可靠性.
为研究人员研究 lncRNAs 在人类疾病中的作用提供一个有价值的工具.

主要方法:

LDA-GARB集成非负矩阵因子化用于线性特征提取和图形自编码器用于lncRNAs和疾病的非线性特征提取.
它计算lncRNA和疾病相似性,以增强特征表示.
采用噪声强度梯度增强模型,从组合特征中预测潜在的LDA.

主要成果:

在各种交叉验证实验中,LDA-GARB在LDA预测方面表现优越,与几种最先进的方法相比.
该框架在不平衡的数据集上显示出强度,敏感性分析证实了其组件的有效性.
LDA-GARB成功预测了结直肠癌和乳腺癌的潜在 lncRNA 关联,分别确定了 CCDC26 和 HAR1A.

结论:

LDA-GARB是一种有效和强大的计算工具,用于预测lncRNA与疾病的关联.
该框架有助于进一步了解 lncRNA 功能在疾病发病过程中的作用.
LDA-GARB为识别新型疾病生物标志物和治疗点提供了宝贵的资源.

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