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AMPFLDAP:适应性信息传递和特征融合在异质网络上的LncRNA-疾病协会预测

Yansen Su¹, Jingjing Liu², Qingwen Wu²

¹Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China. suyansen@ahu.edu.cn.

Interdisciplinary sciences, computational life sciences

|April 6, 2024

概括

预测长非编码RNA疾病关联 (LDA) 对于理解疾病机制至关重要. 一个新的自适应信息传递和特征融合 (AMPFLDAP) 模型通过整合网络拓和生物分子特征有效预测LDA.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

了解长非编码RNA与疾病的关联 (LDA) 对于阐明疾病机制和治疗开发至关重要.
对LDA的计算预测对于减少广泛的实验努力至关重要.
现有的基于图形的模型可以通过更有效的属性集成来改进.

研究的目的:

引入一种新的计算模型,即适应性消息传递和特征融合 (AMPFLDAP),用于更好地预测lncRNA与疾病的关联.
开发一种方法,有效地将拓和语义特征集成到异质生物网络中.

主要方法:

使用高斯相互作用配置文件内核相似性构建了一个包含lncRNA,microRNA (miRNA) 和疾病的异质网络.
开发了一种适应型拓信息传递机制,用于在异质网络中的信息聚合.
采用了注意力机制,将拓和语义特征融合为多模式生物分子表示和LDA预测.

主要成果:

与七种最先进的方法相比,拟议的AMPFLDAP模型表现出优越的性能.
实验结果证实了自适应性传递信息和特征融合方法的有效性.
对三种疾病的案例研究验证了AMPFLDAP在预测LDAs方面的实际实用性.

结论:

在预测lncRNA与疾病的关联方面,AMPFLDAP提供了显著的进步.
该模型能够协调多种数据类型,从而提高预测准确度.
这种方法有望加速疾病研究和治疗策略的开发.

关键词:

适应性的元结构搜索搜索.功能融合的特点是:异质网络是异质的网络.在cRNA-疾病关联中.

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