132 related articles for article (PubMed ID: 30106318)
1. Prediction of Microbe-Disease Associations by Graph Regularized Non-Negative Matrix Factorization.
Liu Y; Wang SL; Zhang JF
J Comput Biol; 2018 Aug; ():. PubMed ID: 30106318
[TBL] [Abstract][Full Text] [Related]
2. Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization.
He BS; Peng LH; Li Z
Front Microbiol; 2018; 9():2560. PubMed ID: 30443240
[TBL] [Abstract][Full Text] [Related]
3. Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe-Disease Associations.
Yang X; Kuang L; Chen Z; Wang L
Front Genet; 2021; 12():754425. PubMed ID: 34721543
[TBL] [Abstract][Full Text] [Related]
4. MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.
Liu H; Bing P; Zhang M; Tian G; Ma J; Li H; Bao M; He K; He J; He B; Yang J
Comput Struct Biotechnol J; 2023; 21():1414-1423. PubMed ID: 36824227
[TBL] [Abstract][Full Text] [Related]
5. MCHMDA:Predicting Microbe-Disease Associations Based on Similarities and Low-Rank Matrix Completion.
Yan C; Duan G; Wu FX; Pan Y; Wang J
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(2):611-620. PubMed ID: 31295117
[TBL] [Abstract][Full Text] [Related]
6. Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.
Peng L; Huang L; Tian G; Wu Y; Li G; Cao J; Wang P; Li Z; Duan L
Front Microbiol; 2023; 14():1244527. PubMed ID: 37789848
[TBL] [Abstract][Full Text] [Related]
7. Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.
Xu D; Xu H; Zhang Y; Gao R
Front Microbiol; 2022; 13():834982. PubMed ID: 35369503
[TBL] [Abstract][Full Text] [Related]
8. MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.
Hua M; Yu S; Liu T; Yang X; Wang H
Interdiscip Sci; 2022 Sep; 14(3):669-682. PubMed ID: 35428964
[TBL] [Abstract][Full Text] [Related]
9. MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.
Xu D; Xu H; Zhang Y; Wang M; Chen W; Gao R
J Transl Med; 2021 Feb; 19(1):66. PubMed ID: 33579301
[TBL] [Abstract][Full Text] [Related]
10. A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.
Zou S; Zhang J; Zhang Z
PLoS One; 2017; 12(9):e0184394. PubMed ID: 28880967
[TBL] [Abstract][Full Text] [Related]
11. BRWMDA:Predicting Microbe-Disease Associations Based on Similarities and Bi-Random Walk on Disease and Microbe Networks.
Yan C; Duan G; Wu FX; Pan Y; Wang J
IEEE/ACM Trans Comput Biol Bioinform; 2020; 17(5):1595-1604. PubMed ID: 30932846
[TBL] [Abstract][Full Text] [Related]
12. SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM.
Wang F; Yang H; Wu Y; Peng L; Li X
Front Microbiol; 2023; 14():1207209. PubMed ID: 37415823
[TBL] [Abstract][Full Text] [Related]
13. A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.
Wang L; Wang Y; Li H; Feng X; Yuan D; Yang J
Front Microbiol; 2019; 10():684. PubMed ID: 31024481
[TBL] [Abstract][Full Text] [Related]
14. A weighted non-negative matrix factorization approach to predict potential associations between drug and disease.
Wang MN; Xie XJ; You ZH; Ding DW; Wong L
J Transl Med; 2022 Dec; 20(1):552. PubMed ID: 36463215
[TBL] [Abstract][Full Text] [Related]
15. MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation.
Xiang H; Guo R; Liu L; Guo T; Huang Q
Front Microbiol; 2023; 14():1216811. PubMed ID: 37389340
[TBL] [Abstract][Full Text] [Related]
16. MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.
Chen Z; Zhang L; Li J; Fu M
Front Microbiol; 2024; 15():1353278. PubMed ID: 38371933
[TBL] [Abstract][Full Text] [Related]
17. Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.
He BS; Qu J; Zhao Q
Front Genet; 2018; 9():303. PubMed ID: 30131824
[TBL] [Abstract][Full Text] [Related]
18. DNRLMF-MDA:Predicting microRNA-Disease Associations Based on Similarities of microRNAs and Diseases.
Yan C; Wang J; Ni P; Lan W; Wu FX; Pan Y
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):233-243. PubMed ID: 29990253
[TBL] [Abstract][Full Text] [Related]
19. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.
Xiao Q; Luo J; Liang C; Cai J; Ding P
Bioinformatics; 2018 Jan; 34(2):239-248. PubMed ID: 28968779
[TBL] [Abstract][Full Text] [Related]
20. Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network.
Lei X; Wang Y
Front Microbiol; 2020; 11():579. PubMed ID: 32351464
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]