... to exploit known regulatory knowledge, which is essential for understanding cell functions. This limitation hinders their ability to provide comprehensive and accurate insights into cells. Here, we propose FactVAE, an innovative factorized variational autoencoder designed for the robust and accurate...

... normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization...

... accumulation. This study explored the positive matrix factorization (PMF) receptor model to identify other possible sources of mercury contamination in two major mining communities (Kenyasi and Obuasi) and a commercial city (Sunyani) in Ghana. The mercury concentrations across the three study areas showed...

... their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF). A unitization strategy was proposed to mitigate the impact of dropout events...

...-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion...

... frameworks to predict new microbe–disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction...

... establishing connections between GD and GF, e.g. [ 8 , 28 ], which show that for sufficiently small step-size, GD and GF exhibit similar behaviours. In this paper, we show that GF with WN, when applied to the standard diagonal linear model for vector/matrix factorization, achieves implicit bias/regularization...

...-factorization, affinity graph learning captures relations among spots by integrating spatial and expression information, and down-stream analysis is executed on the affinity graph to identify spatial patterns. Figure 2 Performance of ST-SCSR on ST datasets from normal brain tissues generated by 10 Visium...

... matrix for Lactobacillus subtypes and Bifidobacterium species, we used non-negative matrix factorization (NMF) to identify probiotic signatures. The NMF process starts by decomposing the probiotics abundance matrix M―comprising n probiotics species or subtypes across...

... of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover...

... and their biological interpretation. MRSA persistence tensor factorization CD4+ T cells granulocytes NIH 10.13039/501100012264 U19-AI172713 U01-AI124319 R01-AI068804 R33-AI111661 U01-AI124319 U19AI128913 UCLA CTSI KL2-TR001882-05 Significance Statement Despite laboratory susceptibility, gold-standard...

... is set to 1e-05 by default), i.e. (6) where and are the matrices at the t-th and the (t-1)-th iterations. To determine the number of factors k and the regularization parameters , and , we used five-fold cross-validation by adapting the way of Elyanow et...

... as a determinant of clinker exposure. Methods The elemental composition (water and acid-soluble fractions separately) of 1,227 personal thoracic workplace samples was analyzed by positive matrix factorization (PMF) to determine the contribution of different sources to the composition of airborne particulate...

..., including millions of nodes and tens of millions of edges. Accordingly, the computational complexity is considered an important factor in community detection algorithms. Most methods fail to find the community structure in real time, because they have nonlinear computational complexity [ 22 ]. In addition...

... MiRNA circRNA–miRNA association large-scale information network embedding graph factorization gradient boosting decision tree Natural Science Foundation of Guangxi Province 10.13039/100012547 2022JJD170019 2023JJA170118 2021AC19394 Natural Science Foundation of Shandong 10.13039...

... and an additive L₂ penalty on the terms in the low-rank factorization, and this leads to an alternative form of our objective (Equation  2 ), (3) $$\begin{eqnarray} &&\min _{\substack{\lbrace \mathbf {U}_B^{(k)},{\bf V}_B^{(k)}\rbrace ^K_{k=1}, \lbrace \mathbf {U}_S^{(l)},{\bf V}_S...

... by reconstructing the expression values between genes and cells. Specifically, a heterogeneous graph of genes and cells is first constructed, and then a graph convolutional network (GCN) is utilized to perform matrix factorization (MF) and learn the embeddings of genes and cells. At the second stage, the gene...

... cohorts has further helped to understand the predictive capabilities of clinical and lifestyle markers, especially in CVD. ^{11 , 12} Subgraph augmented non-negative matrix factorization (SANMF) is a derivative of sparse non-negative matrix factorization, which is a powerful...

... data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii...

... using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep–wake cycles of individuals with highly irregular sleep–wake patterns. The model consists of two hidden...