| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Thirunavukarasu Balasubramaniam, Richi Nayak, Khanh Luong, Md Abul Basha. Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization. Social network analysis and mining. vol 11. issue 1. 2021-06-22. PMID:34149960. |
moreover, the coupled matrix tensor representation has improved the quality of topics discovered from unlabelled data up to 4% by leveraging the semantic similarity of terms in labelled data. |
2021-06-22 |
2023-08-13 |
Not clear |
| Sun Zheng, Yan Xiangyan. Image reconstruction based on compressed sensing for sparse-data endoscopic photoacoustic tomography. Computers in biology and medicine. vol 116. 2021-06-21. PMID:32001014. |
the sparse representation of the complete acoustic signals is then optimally obtained based on the sparse measurements and a sensing matrix. |
2021-06-21 |
2023-08-13 |
Not clear |
| Joshua Schrie. Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)? Journal of chemical information and modeling. vol 60. issue 8. 2021-06-17. PMID:32668151. |
coulomb matrix eigenvalues (cmes) are global 3d representations of molecular structure, which have been previously used to predict atomization energies, prioritize geometry searches, and interpret rotational spectra. |
2021-06-17 |
2023-08-13 |
Not clear |
| Abhishek Aggarwal, Vinayak Vinayak, Saientan Bag, Chiranjib Bhattacharyya, Umesh V Waghmare, Prabal K Mait. Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model. Journal of chemical information and modeling. vol 61. issue 1. 2021-06-17. PMID:33320660. |
using the coulomb matrix representation which encodes the atomic identities and coordinates of the dna base pairs to prepare the input dataset, we train a feedforward neural network model. |
2021-06-17 |
2023-08-13 |
Not clear |
| Seungeui Choi, Saravanan Parameswaran, Jun-Ho Cho. Effects of molecular shape on alcohol aggregation and water hydrogen bond network behavior in butanol isomer solutions. Physical chemistry chemical physics : PCCP. vol 23. issue 23. 2021-06-16. PMID:34075966. |
by employing the graph theoretical analysis such as the degree distribution and the eigenvalue spectrum from the adjacency matrix in the graphical representation of the alcohol h-bond network, we show that the tert-butanol aggregates have a different morphological structure from that of the other three butanol isomers in aqueous solution. |
2021-06-16 |
2023-08-13 |
Not clear |
| Chengkui Xiahou, J N L Conno. Glories, hidden rainbows and nearside-farside interference effects in the angular scattering of the state-to-state H + HD → H Physical chemistry chemical physics : PCCP. vol 23. issue 23. 2021-06-16. PMID:34096934. |
we also use window representations of the scattering matrix, which give rise to truncated pws, (b) six asymptotic (semiclassical) small-angle glory theories and four n rainbow theories, (c) we introduce "coroglo" tests, which let us distinguish between glory and corona scattering at small angles for legendre pws, (d) the semiclassical optical model (som) of herschbach is employed to understand structure in the dcss at intermediate and large angles. |
2021-06-16 |
2023-08-13 |
Not clear |
| Tao Zhang, Yang Cong, Gan Sun, Jiahua Don. Visual-Tactile Fused Graph Learning for Object Clustering. IEEE transactions on cybernetics. vol PP. 2021-06-16. PMID:34133303. |
specifically, mr focuses on learning modality-specific representations for visual-tactile data, where deep non-negative matrix factorization (nmf) is adopted to extract the hidden information behind each modality. |
2021-06-16 |
2023-08-13 |
Not clear |
| Lin Liang, Xingyun Ding, Fei Liu, Yuanming Chen, Haobin We. Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis. Sensors (Basel, Switzerland). vol 21. issue 11. 2021-06-15. PMID:34070578. |
non-negative matrix factorization (nmf), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time-frequency representation. |
2021-06-15 |
2023-08-13 |
Not clear |
| José Carlos Ferrão, Mónica Duarte Oliveira, Filipe Janela, Henrique M G Martins, Daniel Gartne. Can structured EHR data support clinical coding? A data mining approach. Health systems (Basingstoke, England). vol 10. issue 2. 2021-06-11. PMID:34104432. |
the methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. |
2021-06-11 |
2023-08-13 |
Not clear |
| Jin Li, Jingru Wang, Hao Lv, Zhuoxuan Zhang, Zaixia Wan. IMCHGAN: Inductive Matrix Completion with Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2021-06-11. PMID:34115592. |
then, the learned latent features are fed into the inductive matrix completion (imc) prediction score model which computes the best projection from drug space onto target space and output dti score via the inner product of projected drug and target feature representations. |
2021-06-11 |
2023-08-13 |
Not clear |
| Yulian Ding, Xiujuan Lei, Bo Liao, Fangxiang W. Predicting miRNA-Disease Associations Based on Multi-View Variational Graph Auto-Encoder with Matrix Factorization. IEEE journal of biomedical and health informatics. vol PP. 2021-06-10. PMID:34111017. |
simultaneously, a non-negative matrix factorization is conducted on the mirna-disease association matrix to get the linear representations of mirnas and diseases. |
2021-06-10 |
2023-08-13 |
human |
| A P Yusefovich, I V Artyushin, A A Raspopova, A A Bannikova, S V Krusko. An Attempt to Reconstruct the Phylogeny of the Hipposideros Leaf-nosed Bats Based on Nuclear Gene Markers. Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections. vol 493. issue 1. 2021-06-07. PMID:32894429. |
in the present study we compare the results of phylogenetic reconstructions, based on mitochondrial, on single nuclear genes and on multiple nuclear markers analyzed using the mrp algorithm (matrix representation with parsimony) for representative species from all the main species groups. |
2021-06-07 |
2023-08-13 |
Not clear |
| Esther A Guzmán, Tara P Pitts, Priscilla L Winder, Amy E Wrigh. The Marine Natural Product Furospinulosin 1 Induces Apoptosis in MDA-MB-231 Triple Negative Breast Cancer Cell Spheroids, But Not in Cells Grown Traditionally with Longer Treatment. Marine drugs. vol 19. issue 5. 2021-06-07. PMID:33924764. |
cancer cells grown in spheroid conditions interact with each other and the extracellular matrix, providing a better representation of the in vivo environment than two-dimensional cultures and are a more clinically relevant model. |
2021-06-07 |
2023-08-13 |
Not clear |
| Raffaele Borrelli, Sergey Dolgo. Expanding the Range of Hierarchical Equations of Motion by Tensor-Train Implementation. The journal of physical chemistry. B. vol 125. issue 20. 2021-05-27. PMID:33980011. |
the non-equilibrium thermo-field dynamics formulation of the hierarchical equations of motion combined with the tensor-train representation of the density matrix is discussed, and a new numerical integration scheme is introduced. |
2021-05-27 |
2023-08-13 |
Not clear |
| Ying Qian, Xuelian Li, Qian Zhang, Jiongmin Zhan. SPP-CPI: Predicting Compound-Protein Interactions Based on Neural Networks. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2021-05-27. PMID:34043511. |
on the other hand, a distance matrix does not require complex data preprocessing for the molecular structure as the molecular graph representation, and is easier to obtain; 2) it uses spp(spatial pyramid pooling)-net to extract compound features, which has been successfully applied in image classification; and 3) it extracts protein features through the natural language processing method (doc2vec) to obtain sequence semantic information. |
2021-05-27 |
2023-08-13 |
Not clear |
| Lei Xing, Badong Chen, Shaoyi Du, Yuantao Gu, Nanning Zhen. Correntropy-Based Multiview Subspace Clustering. IEEE transactions on cybernetics. vol 51. issue 6. 2021-05-24. PMID:31794416. |
compared with the single-view subspace clustering, it is challenging to efficiently learn the structure of the representation matrix from each view and make use of the extra information embedded in multiple views. |
2021-05-24 |
2023-08-13 |
Not clear |
| Xuelong Li, Qi Wang, Feiping Nie, Mulin Che. Locality Adaptive Discriminant Analysis Framework. IEEE transactions on cybernetics. vol PP. 2021-05-21. PMID:33502996. |
in order to directly process the data with matrix representation, such as images, the 2-d lada (2dlada) is also developed. |
2021-05-21 |
2023-08-13 |
Not clear |
| Junyang Chen, Zhiguo Gong, Wei Wang, Weiwen Liu, Xiao Don. CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings. IEEE transactions on neural networks and learning systems. vol PP. 2021-05-21. PMID:33566768. |
in this article, we propose a unified model based on matrix factorization (mf), named collaborative representation learning (crl), which: 1) considers complementary global and local information simultaneously and 2) models topics and learns network embeddings collaboratively. |
2021-05-21 |
2023-08-13 |
Not clear |
| Di Wang, Songwei Han, Quan Wang, Lihuo He, Yumin Tian, Xinbo Ga. Pseudo-Label Guided Collective Matrix Factorization for Multiview Clustering. IEEE transactions on cybernetics. vol PP. 2021-05-21. PMID:33606648. |
to address these challenges, a pseudo-label guided collective matrix factorization (plcmf) method that jointly learns latent representations and cluster structures is proposed in this article. |
2021-05-21 |
2023-08-13 |
Not clear |
| Di Wang, Songwei Han, Quan Wang, Lihuo He, Yumin Tian, Xinbo Ga. Pseudo-Label Guided Collective Matrix Factorization for Multiview Clustering. IEEE transactions on cybernetics. vol PP. 2021-05-21. PMID:33606648. |
then, it adds a pseudo-label constraint on collective matrix factorization to learn unified latent representations, which preserve the intraview and interview similarities simultaneously. |
2021-05-21 |
2023-08-13 |
Not clear |