Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms.

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms and L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.

Introduction: 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. Methods: 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 Lp,q-norms to the projection matrix to ensure the interpretability and sparsity of the model. Results: 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.

Molecular dynamics simulations of He bubble nucleation at grain boundaries.

The nucleation behavior of He bubbles in single-crystal (sc) and nano-grain body-centered-cubic (bcc) Mo is simulated using molecular dynamics (MD) simulations, focusing on the effects of the grain boundary (GB) structure. In sc Mo, the nucleation behavior of He bubbles depends on irradiation conditions. He bubbles nucleate by either clustering of He atoms with pre-existing vacancies or self-interstitial-atom (SIA) punching without initial vacancies. In nano-grain Mo, strong precipitation of He at the GBs is observed, and the density, size and spatial distribution of He bubbles vary with the GB structure. The corresponding He bubble density is higher in nano-grain Mo than that in sc Mo and the average bubble size is smaller. In the GB plane, He bubbles distribute along the dislocation cores for GBs consisting of GB dislocations and randomly for those without distinguishable dislocation structures. The simulation results in nano-grain Mo are in agreement with previous experiments in metal nano-layers, and they are further explained by the effect of excess volume associated with the GBs.