Abstract

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

Graphic Abstract

摘要

用于预测潜在微生物与疾病关联的计算方法通常依赖于微生物和疾病之间的相似性信息。因此，通过整合多种类型的相似信息来获取可靠的相似信息非常重要。然而，现有的相似性融合方法没有考虑相似性网络的多阶融合。为了解决这个问题，提出了一种具有多阶相似性融合学习的线性邻域标签传播新方法（MOSFL-LNP）来预测潜在的微生物与疾病的关联。多阶融合学习包括两部分：低阶全局学习和高阶特征学习。低阶全局学习用于从多个相似源获取共同的潜在特征。高阶特征学习依赖于相邻节点之间的交互来识别高阶相似性并学习更深层次的交互网络结构。将系数分配给不同的高阶特征学习模块，以平衡从不同阶学习到的相似性，增强融合网络的鲁棒性。总体而言，通过将低阶全局学习与高阶特征学习相结合，多阶融合学习可以捕获不同相似网络的共享特征和独特特征，从而更准确地预测微生物与疾病的关联。与其他六种先进方法相比，MOSFL-LNP 在留一交叉验证和 5 倍验证框架中表现出优越的预测性能。在案例研究中，预测的10种与哮喘和1型糖尿病相关的微生物的准确率分别高达90%和100%。

图文摘要