Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Hailang Wu, Yonghe Liu, Bingxuan Yu, Chaoqian Li
Non-negative reduced biquaternion matrix factorization (NRBMF) uses the product of reduced biquaternion (RB) matrices to incorporate the non-negativity constraints of color image pixels into the factorization process. However, NRBMF mainly focuses on reconstruction accuracy and does not exploit the local geometric…
Weilai Chi, Ying Zheng, Huaying Fang, Shi Shi + 1 more
Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of cellular heterogeneity, yet accurate cell-type annotation remains challenging due to data sparsity, technical noise, and variability across tissues, platforms, and species. Many existing annotation tools depend on a single form of prior…
Juan Liang, Jiuxi Huang, Chenxi Xi, Yun Wang + 2 more
The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix…
Anqi Liu, Ran Gu, Rui-Jin Zhang
Community detection in complex networks is frequently challenged by incomplete or noisy adjacency matrices. Traditional symmetric nonnegative matrix factorization methods typically rely on zero-imputation for unobserved entries, which compromises clustering reliability. This paper proposes a Masked Symmetric…
Rok Hribar, Gregor Papa, Janez Povh, Andrej Kastrin
We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-negative and orthogonal factor $G$. This model is motivated by clustering and network…
Ryan Swart, Johannes Brust
Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$. It has broad applications in graph clustering and machine learning. In contrast to the NMF, projected gradient methods for the symmetric problem had been associated with slow convergence.…
Jin Deng, Junjie Lan, Ruolan Du, Tao Xu + 4 more
The high recurrence rate of tumor limits the growth of precision medicine, whereas the exploration of correlations in multimodal data enables mining of features linked to tumor recurrence, ultimately identifying prospective biomarkers. Nevertheless, existing multimodal approaches centered on genetic molecular data…
Yulin He, Ran Gu
Determining the optimal factorization rank is a fundamental yet notoriously challenging problem in Nonnegative Matrix Factorization, conventionally relying on heuristic thresholds or computationally expensive cross-validation. We introduce column $\ell_{2,0}$-norm regularization on both factor matrices to promote rank…
Nguyen, Manh, Pimentel-Alarcón, Daniel
Nonnegative matrix factorization (NMF) is a widely used tool for learning partsbased, low-dimensional representations of nonnegative data, with applications in vision, text, and bioinformatics [1, [2]]. In clustering applications, orthogonal NMF (ONMF) variants further impose (approximate) orthogonality on the…
Ragnhild Laursen, Han Chen, Jack Demaray, Karin Pelka + 1 more
Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales…
Anish Karpurapu, Charles Gersbach, Rohit Singh
Non-negative matrix factorization (NMF) is a foundational dimensionality-reduction method in single-cell transcriptomics, valued for its interpretable gene programs. However, in case-control settings common in perturbation and disease research, standard NMF conflates quantitative shifts in program usage with…
Jing Wu, Wenbo Li, Lijun Su, Huiru Wang + 2 more
In this paper, we present a modified nonmonotone line search algorithm that employs a variable parameter to control the degree of nonmonotonicity. This modification enhances both the probability of identifying the global minimum and the rate of convergence. Within the framework of alternating nonnegative least squares…
Ko Abe, Shintaro Yuki, Teppei Shimamura
Combinatorial indexing-based single-cell RNA sequencing methods such as sci-RNA-seq and sci-RNA-seq3 now enable the profiling of millions of cells, producing expression matrices that are both extremely sparse and high-dimensional. Conventional nonnegative matrix factorization (NMF) provides an interpretable framework…
Vladi Sorkin, Yakir Menahem, Dmitry Patashov, Michal Balberg + 1 more
We investigate whether bilingual versus monolingual language environments in early infancy are associated with differences in intrinsic functional organization measured from resting-state fNIRS connectivity. Using the RS4 infant resting-state fNIRS cohort (HbO), we studied two complementary subject-level…
Anirban Chakraborty, Brian Neelon, Andrew Lawson, Peggi Angel + 2 more
As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial…
Chengyi Ma, Jiadong Mao, Kim-Anh Lê Cao
Integrating histological images with gene expression data offers a promising approach for linking tissue morphologies to molecular signatures and improving disease subtyping. However, such integration remains challenging due to the high dimensionality of these datasets, cross-modal heterogeneity, and limited…
Samuel Anyaso-Samuel, Shilan Li, Giovanny Herrera Ossa, Emily Vogtmann + 15 more
Biological traits such as genes, metabolites, and microbial taxa interact within complex networks, yet how genomic factors shape these interactions remains poorly understood. Here, we introduce GFBioNet, a computationally efficient method for identifying factors that modulate direct associations between biological…
Xingsu Wang, Yanyan Chen, Dian Huang, Zhen Ju + 3 more
Rare-cell identification is essential for dissecting disease mechanisms and developmental programs. Existing methods mostly rely on fixed-size neighbourhood graphs to separate rare-cell populations in single-cell expression data, which may embed rare cells into dominant clusters under varying sampling densities. This…
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Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
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Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…