| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Akshatha Prasanna, Vidya Niranja. MutVis: Automated framework for analysis and visualization of mutational signatures in pathogenic bacterial strains. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. vol 91. 2021-09-06. PMID:33689914. |
mutvis supports variant calling, transition (ti) and transversion (tv) graphical representation, generation of mutational count matrix, graphical visualization of base-pair substitution spectrum (bpss) and mutation signatures extraction. |
2021-09-06 |
2023-08-13 |
Not clear |
| Sehwan Moon, Hyunju Le. JDSNMF: Joint Deep Semi-Non-Negative Matrix Factorization for Learning Integrative Representation of Molecular Signals in Alzheimer's Disease. Journal of personalized medicine. vol 11. issue 8. 2021-08-31. PMID:34442330. |
jdsnmf: joint deep semi-non-negative matrix factorization for learning integrative representation of molecular signals in alzheimer's disease. |
2021-08-31 |
2023-08-13 |
human |
| Bao-Yu Liu, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, Philip S Y. Multiview Clustering via Proximity Learning in Latent Representation Space. IEEE transactions on neural networks and learning systems. vol PP. 2021-08-27. PMID:34432638. |
for another, through conducting the latent representation learning and consensus proximity learning simultaneously, mlpl learns a consensus proximity matrix with k connected components to output the clustering result directly. |
2021-08-27 |
2023-08-13 |
Not clear |
| Mulin Chen, Maoguo Gong, Xuelong L. Feature Weighted Non-Negative Matrix Factorization. IEEE transactions on cybernetics. vol PP. 2021-08-26. PMID:34437084. |
non-negative matrix factorization (nmf) is one of the most popular techniques for data representation and clustering and has been widely used in machine learning and data analysis. |
2021-08-26 |
2023-08-13 |
Not clear |
| Adam Winchell, Andrew Lan, Michael Moze. Highlights as an Early Predictor of Student Comprehension and Interests. Cognitive science. vol 44. issue 11. 2021-08-23. PMID:33191526. |
using multiple representations of the highlighting patterns, we built probabilistic models to predict quiz performance and matrix factorization models to predict what content would be highlighted in one passage from highlights in other passages. |
2021-08-23 |
2023-08-13 |
human |
| Onur Çaylak, Björn Baumeie. Machine Learning of Quasiparticle Energies in Molecules and Clusters. Journal of chemical theory and computation. vol 17. issue 8. 2021-08-14. PMID:34314186. |
coulomb matrix, bag-of-bond, and bond-angle-torsion representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and kohn-sham orbital energies, both of which are readily available from baseline calculations at the level of density functional theory (dft). |
2021-08-14 |
2023-08-13 |
Not clear |
| Miguel Angel Funes-Lora, Brian J Thelen, Albert J Shih, James Hamilton, Nirmala Rajaram, Jingxuan Lyu, Yihao Zheng, Timothy Morgan, William F Weitze. Ultrasound Measurement of Vascular Distensibility Based on Edge Detection and Speckle Tracking Using Ultrasound DICOM Data. ASAIO journal (American Society for Artificial Internal Organs : 1992). 2021-08-12. PMID:34380948. |
canny edge detector, vandermonde matrix representation, kanade lucas tomasi algorithm with pyramidal segmentation, and penalized least squares technique identifies the vessel lumen edge, track the vessel diameter, detrend the signal and find peaks and valleys when the vessel is fully distended or contracted. |
2021-08-12 |
2023-08-13 |
Not clear |
| Aymeric Le Gratiet, Luca Lanzano, Artemi Bendandi, Riccardo Marongiu, Paolo Bianchini, Colin Sheppard, Alberto Diaspr. Phasor approach of Mueller matrix optical scanning microscopy for biological tissue imaging. Biophysical journal. vol 120. issue 15. 2021-08-11. PMID:34224693. |
in this work, we propose a new, to our knowledge, representation dedicated to the study of biological tissues that combines mueller matrix microscopy with a phasor approach. |
2021-08-11 |
2023-08-13 |
Not clear |
| Anthony Steed, Eyal Ofek, Mike Sinclair, Mar Gonzalez-Franc. A mechatronic shape display based on auxetic materials. Nature communications. vol 12. issue 1. 2021-08-10. PMID:34362893. |
besides their complexity and high cost, these matrix displays suffer from sharp edges due to the discreet representation which reduces their ability to render a large continuous surface when sliding the hand. |
2021-08-10 |
2023-08-13 |
Not clear |
| Meenu Gupta, Hao Wu, Simrann Arora, Akash Gupta, Gopal Chaudhary, Qiaozhi Hu. Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection. Journal of healthcare engineering. vol 2021. 2021-08-10. PMID:34367540. |
three machine learning classification models, namely, logistic regression (lr), random forest (rf), and xgboost (xgb), along with the recurrent neural network (rnn) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. |
2021-08-10 |
2023-08-13 |
Not clear |
| Zhikui Chen, Shan Jin, Runze Liu, Jianing Zhan. A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning. Frontiers in neurorobotics. vol 15. 2021-08-07. PMID:34354579. |
a deep non-negative matrix factorization model for big data representation learning. |
2021-08-07 |
2023-08-13 |
Not clear |
| Zhikui Chen, Shan Jin, Runze Liu, Jianing Zhan. A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning. Frontiers in neurorobotics. vol 15. 2021-08-07. PMID:34354579. |
to alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper. |
2021-08-07 |
2023-08-13 |
Not clear |
| Zhikui Chen, Shan Jin, Runze Liu, Jianing Zhan. A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning. Frontiers in neurorobotics. vol 15. 2021-08-07. PMID:34354579. |
furthermore, to train the deep matrix factorization architecture, an interpretability loss is defined, including a symmetric loss, an apposition loss, and a non-negative constraint loss, which can ensure the knowledge transfer from the supervisor network to the student network, enhancing the robustness of deep representations. |
2021-08-07 |
2023-08-13 |
Not clear |
| Ying Guo, Yan-Fang Wang, Sheng-Li Zhan. A novel way to numerically characterize DNA sequences and its application. International journal of quantum chemistry. vol 111. issue 14. 2021-08-04. PMID:32327765. |
instead of calculating the leading eigenvalues of the matrix for graphical representation, we computed curvature and torsion of curves as the descriptor to numerically characterize dna sequences. |
2021-08-04 |
2023-08-13 |
Not clear |
| Aiping Huang, Weiling Chen, Tiesong Zhao, Chang Wen Che. Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-08-02. PMID:34310300. |
moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. |
2021-08-02 |
2023-08-13 |
Not clear |
| Aiping Huang, Weiling Chen, Tiesong Zhao, Chang Wen Che. Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-08-02. PMID:34310300. |
the solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or k-means for multi-view clustering. |
2021-08-02 |
2023-08-13 |
Not clear |
| Wei Zhang, Xiaoli Xue, Xiaoying Zheng, Zizhu Fa. NMFLRR: Clustering scRNA-seq data by integrating non-negative matrix factorization with low rank representation. IEEE journal of biomedical and health informatics. vol PP. 2021-07-26. PMID:34310328. |
nmflrr: clustering scrna-seq data by integrating non-negative matrix factorization with low rank representation. |
2021-07-26 |
2023-08-13 |
Not clear |
| Wei Zhang, Xiaoli Xue, Xiaoying Zheng, Zizhu Fa. NMFLRR: Clustering scRNA-seq data by integrating non-negative matrix factorization with low rank representation. IEEE journal of biomedical and health informatics. vol PP. 2021-07-26. PMID:34310328. |
we present a new computational framework to identify cell types by integrating low-rank representation (lrr) and nonnegative matrix factorization (nmf); this framework is named nmflrr. |
2021-07-26 |
2023-08-13 |
Not clear |
| Simon Wiedemann, Klaus-Robert Muller, Wojciech Same. Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE transactions on neural networks and learning systems. vol 31. issue 3. 2021-07-12. PMID:31150347. |
in order to efficiently exploit such matrices, one usually relies on, inter alia, sparse matrix representations. |
2021-07-12 |
2023-08-13 |
Not clear |
| Ya-Li Zhu, Sha-Sha Yuan, Jin-Xing Li. Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis. Interdisciplinary sciences, computational life sciences. 2021-07-07. PMID:34231183. |
in this paper, we propose a novel clustering method similarity and dissimilarity regularized nonnegative matrix factorization (sdcnmf) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. |
2021-07-07 |
2023-08-13 |
Not clear |