Chen Ziwei, Zhang Liangzhe, Li Jingyi, Fu Mingyang
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Front Microbiol. 2024 Feb 2;15:1353278. doi: 10.3389/fmicb.2024.1353278. eCollection 2024.
A growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.
In this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. To obtain unobserved associations in the microbe and disease views, we extract different latent features based on the geometrical structure of microbes and diseases, and project multi-modal latent features into a common subspace. Next, we introduce graph regularization to preserve the local manifold structure of Gaussian interaction profile kernel similarity and add -norms to the projection matrix to ensure the interpretability and sparsity of the model.
The AUC values for global leave-one-out cross-validation and 5-fold cross validation implemented by MLFLHMDA are 0.9165 and 0.8942+/-0.0041, respectively, which perform better than other existing methods. In addition, case studies of different diseases have demonstrated the superiority of the predictive power of MLFLHMDA. The source code of our model and the data are available on https://github.com/LiangzheZhang/MLFLHMDA_master.
越来越多的研究表明，微生物在人类健康中起着至关重要的作用。微生物群落的失衡与人类疾病密切相关，识别微生物与疾病之间的潜在关系有助于阐明疾病的发病机制。然而，基于生物学或临床实验的传统方法成本高昂，因此使用计算模型来预测潜在的微生物-疾病关联具有重要意义。
在本文中，我们提出了一种名为MLFLHMDA的新型计算模型，该模型基于多视图潜在特征学习方法来预测人类潜在的微生物-疾病关联。具体而言，我们根据人类微生物-疾病关联数据库中已知的微生物-疾病关联，计算疾病与微生物之间的高斯交互轮廓核相似度，并对所得的微生物-疾病关联矩阵进行预处理步骤，即加权K近邻（WKNKN）以降低微生物-疾病关联矩阵的稀疏性。为了获得微生物和疾病视图中未观察到的关联，我们基于微生物和疾病的几何结构提取不同的潜在特征，并将多模态潜在特征投影到一个公共子空间中。接下来，我们引入图正则化来保留高斯交互轮廓核相似度的局部流形结构，并在投影矩阵上添加范数以确保模型的可解释性和稀疏性。
MLFLHMDA实施的全局留一法交叉验证和5折交叉验证的AUC值分别为0.9165和0.8942±0.0041，其性能优于其他现有方法。此外，不同疾病的案例研究证明了MLFLHMDA预测能力的优越性。我们模型的源代码和数据可在https://github.com/LiangzheZhang/MLFLHMDA_master上获取。
