DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization by Nahla Mohamed Ahmed, Ling Chen, Yulong Wang, Bin Li, Yun Li, Wei Liu

“…A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. …”
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Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data by Hirotaka Kaji, Kazushi Ikeda

“…This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. …”
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Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach by Meng Zhang, Joel Parker, Lingling An, Yiwen Liu, Xiaoxiao Sun

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Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise by Suhyeon Park, Jongwon Seok, Jungpyo Hong

Subjects: “…non-negative matrix factorization (NMF)…”
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Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms by Zheng Yu, Xiaoyan Zheng, Jiaqi Sun, Pengfei Zhang, Ying Zhong, Xingyu Lv, Hongwen Yuan, Fanrong Liang, Dexian Wang, Jie Yang

“…We introduce a novel Non-negative Matrix Factorization (NMF)-based Ensemble algorithm (NMFE). …”
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Multi-view Clustering: A Survey by Yan Yang, Hao Wang

“…Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. …”
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Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function by Matin Pashaian, Sanaz Seyedin

“…We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. …”
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Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks by Xueting Liao, Danyang Zheng, Xiaojun Cao

“…In this paper, we propose a Tripartite Graph Clustering for Pandemic Data Analysis (TGC-PDA) framework that builds on the proposed models and analysis: (1) tripartite graph representation, (2) non-negative matrix factorization with regularization, and (3) sentiment analysis. …”
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Assessing brain-muscle networks during motor imagery to detect covert command-following by Emilia Fló, Daniel Fraiman, Jacobo Diego Sitt

“…Brain-muscle networks were obtained using non-negative matrix factorization (NMF) of the coherence spectra for all the channel pairs. …”
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Exploring Topic Coherence With PCC-LDA and BERT for Contextual Word Generation by Sandeep Kumar Rachamadugu, T. P. Pushphavathi, Surbhi Bhatia Khan, Mohammad Alojail

“…The above results of the topic-level analysis indicate that PCC-LDA consistency topics perform better than LDA and NMF(non-negative matrix factorization Technique) by at least 15.4%,12.9%(<inline-formula> <tex-math notation="LaTeX">$k = 5$ </tex-math></inline-formula>) and up to nearly 12.5% and 11.8% (<inline-formula> <tex-math notation="LaTeX">$k = 10$ </tex-math></inline-formula>) respectively, where k represents the number of topics.…”
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Facial expression recognition through muscle synergies and estimation of facial keypoint displacements through a skin-musculoskeletal model using facial sEMG signals by Lun Shu, Victor R. Barradas, Zixuan Qin, Yasuharu Koike

“…For the facial expression recognition task, we studied the coordination patterns of seven muscles, expressed as three muscle synergies extracted through non-negative matrix factorization, during the execution of six basic facial expressions. …”
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Machine learning-driven identification of critical gene programs and key transcription factors in migraine by Lei Zhang, Yujie Li, Yunhao Xu, Wei Wang, Guangyu Guo

“…The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. …”
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Single-cell transcriptomes of dissecting the intra-tumoral heterogeneity of breast cancer microenvironment by Peixian Chen, Kaifeng Liang, Xiaofan Mao, Qiuyuan Wu, Zhiyan Chen, Yabin Jin, Kairong Lin, Tiancheng He, Shuqing Yang, Huiqi Huang, Guolin Ye, Juntao Gao, Dan Zhou, Zhihao Zeng

“…Gene set enrichment analysis (GSEA) based on the top 50 highly NMF (non-negative matrix factorization) score genes in each program depicted the distinct function of each program in breast cancer progression. …”
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The key role of the NUDT3 gene in lung adenocarcinoma progression and its association with the manganese ion metabolism family by Deyong Ge, Xinyu Xu, Liyi Fang, Huihui Tao

“…Single-cell transcriptomic analysis of two normal and four lung adenocarcinoma samples from the GSE149655 dataset was performed using the Seurat package to identify and annotate distinct cell populations, focusing on epithelial and macrophage subtypes. Non-negative matrix factorization (NMF) and gene set variation analysis (GSVA) were applied to assess the functional enrichment and expression profiles of the manganese ion metabolism family across 14 cancers. …”
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Exploration of the mechanism of 5-Methylcytosine promoting the progression of hepatocellular carcinoma by Qiyao Zhang, Zhen Cao, Yuting He, Ziwen Liu, Wenzhi Guo

“…In this study, six pairs of HCC and adjacent tissue samples were subjected to methylated RNA immunoprecipitation sequencing to identify precise m5C loci. Non-negative matrix factorization (NMF) was used to identify HCC subtypes in TCGA-LIHC cohort. …”
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Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing by Anjana Hevaganinge, Eva Lowenstein, Anna Filatova, Mihir Modak, Nandi Thales Mogo, Bryana Rowley, Jenny Yarmowsky, Joshua Ehizibolo, Ravidu Hevaganinge, Amy Musser, Abbey Kim, Anthony Neri, Jessica Conway, Yiding Yuan, Maurizio Cattaneo, Sui Seng Tee, Yang Tao

“…The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). …”
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