ConvNTC:用于检测"A-A-B"类型生物三胞胎的卷积神经张量完成

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Pei Liu^1,2, Xiao Liang¹, Yue Li²

¹Department of Computer Science, College of Computer Science and Electronic Engineering, 116 Lu Shan South Road, Hunan University, Changsha 410082, Hunan, China.

Briefings in bioinformatics

|August 1, 2025

概括

这项研究引入了卷积神经张量完成 (ConvNTC) 来建模复杂的分子相互作用. ConvNTC准确地预测了分子三胞胎,有助于发现疾病机制和药物开发.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
网络科学网络科学

背景情况:

了解分子相互作用是疾病研究和治疗的关键.
"A-A-B"三重组范例模型是特定于背景的分子关系.
基于张量器的方法难以捕捉多线性和非线性相互作用特征.

研究的目的:

开发一个新的框架,卷积神经张量完成 (ConvNTC),用于建模"A-A-B"类型的三重相互作用.
整合多线性和非线性建模方法,以增强三重组预测.
提高识别特定环境分子相互作用的准确性.

主要方法:

提出了一种具有多线性和非线性模块的卷积神经张量完成 (ConvNTC) 框架.
使用张量分解,对因子嵌入有约束.
包含一个嵌入式生成器,卷积编码器和Kolmogorov-Arnold网络 (KAN) 预测器,用于非线性特征映射和关系捕获.

主要成果:

与11种最先进的方法相比,ConvNTC在三倍预测方面表现优越.
在miRNA-miRNA-disease和药物-药物-细胞三重组数据集上进行评估.
确定了乳腺癌中的miRNA-miRNA相互作用和癌细胞系中的协同药物组合的预后值.

结论:

ConvNTC有效地模拟多线性和非线性特征,以准确预测三重组.
该框架显示了促进疾病机制研究和治疗策略开发的潜力.
ConvNTC为分析复杂的生物网络提供了一个强大的工具.

关键词:

深度学习是一种深度学习.药物组合是药物组合.微RNA微RNA相互作用张量器的完成完成.三重的预测预测三重的预测

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