AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction

IF 3.9 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-04-06 DOI:10.1007/s12539-024-00610-5

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Abstract

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP’s effectiveness in the prediction of LDAs.

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AMPFLDAP：用于 LncRNA 与疾病关联预测的异构网络上的自适应信息传递和特征融合

摘要探索长非编码RNA（lncRNA）与疾病之间错综复杂的联系，即lncRNA-疾病关联（LDAs），对于揭示疾病的潜在分子机制和设计切实可行的治疗方法起着不可或缺的关键作用。当务之急是采用计算方法预测 lncRNA 与疾病的关联，以避免多余的实验工作。基于图的学习模型在预测这些关联方面大受欢迎，这主要是因为它们能够利用网络中的节点属性和关系。然而，通过更有效地整合和协调节点属性，这些技术的性能仍有很大的提升空间。在此背景下，我们引入了一种新型模型，即自适应信息传递和特征融合（AMPFLDAP），用于预测异构网络中 lncRNA 与疾病的关联。首先，我们基于已建立的关联，并采用高斯交互轮廓核相似度作为衡量标准，构建了一个涉及lncRNA、microRNA（miRNA）和疾病的异构网络。然后，提出了一种自适应拓扑信息传递机制来解决异构网络的信息聚合问题。基于自适应拓扑信息传递机制，提取了异构网络中节点的拓扑特征。此外，应用注意力机制整合拓扑和语义信息，实现生物分子的多模态特征，并进一步用于预测潜在的 LDA。实验结果表明，所提出的 AMPFLDAP 的性能优于七种最先进的方法。此外，为了验证其在实际应用场景中的有效性，我们进行了涉及三种不同疾病的详细案例研究，最终证明了 AMPFLDAP 在预测 LDA 方面的有效性。

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来源期刊

Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

8.60

自引率

4.20%

发文量

期刊介绍： Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.