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Search · four archives
22 papers · ranked by Valyu relevance
Mahbod Nouri, David Rotermund, Alberto Garcia-Ortiz, Klaus R. Pawelzik
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non-negative matrix factorization (NMF) captures biological constraints of positive…
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…
Giovanni Barbarino, Nicolas Gillis, Subhayan Saha
Minimum-volume nonnegative matrix factorization (min-vol NMF) has been used successfully in many applications, such as hyperspectral imaging, chemical kinetics, spectroscopy, topic modeling, and audio source separation. However, its robustness to noise has been a long-standing open problem. In this paper, we prove that…
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…
Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris
Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems. As the popularity of NMF has…
Cindy Fang, Kelsey D. Montgomery, Sarah E. Maguire, Anthony D. Ramnauth + 8 more
Recent advances in spatially-resolved transcriptomics have enabled profiling of gene expression in a spatial context, which has led to the generation of large-scale single-cell and spatial atlases with computationally-derived cell type or spatial domain labels. An increasingly important task with these data has become…
Nandini Chatterjee, Aleksandr Taraskin, Hridya Divakaran, Natalia Jaeger + 3 more
The rapid evolution of single-cell technologies has generated vast, multimodal datasets encompassing genomic, transcriptomic, proteomic, and spatial information. However, high dimensionality, noise, and computational costs pose significant challenges, often introducing bias through traditional feature selection…
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.…
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…
Yu-Ting Liu, Timothy J Triche, Zachary J DeBruine
Large single-cell atlases now span tens of millions of cells, yet few provide reusable and interpretable reference representations that support direct biological reasoning at atlas-scale. Here, we present an interpretable Non-negative Matrix Factorization reference embedding of 28.5 million healthy cells and…
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…
Eric Weine, Peter Carbonetto, Rafael A. Irizarry, Matthew Stephens
Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist, existing methods assume that the "parts" in the decomposition combine additively. This assumption may be natural in some settings, but not…
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…
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…
Bharat Pratap Chauhan, Projesh Nath Choudhury
Symmetric nonnegative matrix trifactorizations (SN-Trifactorizations) were introduced by Bukovšek-Šmigoc [Linear Algebra Appl. 2023] as a symmetric analogue of nonnegative matrix factorizations. A SN-Trifactorization of a symmetric nonnegative matrix $A$ is of the form $A = BCB^{T},$ where $B$ and $C$ are nonnegative…
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…
Elizaveta Kobeleva, Surahit Chewle, Marius Horch, Marcus Weber
Method for Analyzing Time-Resolved Spectroscopic Data Authors: Elizaveta Kobeleva, Surahit Chewle, Marius Horch, Marcus Weber Time-resolved spectroscopy is a widely used tool for the investigation of physical and chemical processes. Analysis of the results is often challenging due to the inherent complexity of the…
Ari Peden-Asarch, Meredith Weinstock, Kevin R. Coffey, John F. Neumaier
Miniscope calcium imaging provides a unique window into the activity of neurons during behavior while enabling spatial localization of individual cells across time. Despite its potential to revolutionize in vivo imaging alongside the rise of optogenetic tools, miniscopes remain underutilized. This gap may stem from the…
Hongbin Lv, Meixiang Chen, Wen Li
The R-linear convergence of the NQZ algorithm for computing the H-spectral radius of a class of weakly irreducible nonnegative tensors is established by utilizing the directed graphs of tensors. Meanwhile, an upper bound for the root convergence factor R is derived and a general condition ensuring the linear…
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Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
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Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to…