157 related articles for article (PubMed ID: 33119529)
1. NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation.
Yin MM; Liu JX; Gao YL; Kong XZ; Zheng CH
IEEE Trans Cybern; 2022 Jun; 52(6):5079-5087. PubMed ID: 33119529
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Novel human microbe-disease associations inference based on network consistency projection.
Zou S; Zhang J; Zhang Z
Sci Rep; 2018 May; 8(1):8034. PubMed ID: 29795313
[TBL] [Abstract][Full Text] [Related]
5. GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.
Huang YA; Huang ZA; Li JQ; You ZH; Wang L; Yi HC; Yu CQ
BMC Genomics; 2022 Mar; 22(Suppl 1):916. PubMed ID: 35296232
[TBL] [Abstract][Full Text] [Related]
6. Predicting Microbe-Disease Association Based on Multiple Similarities and LINE Algorithm.
Wang Y; Lei X; Lu C; Pan Y
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2399-2408. PubMed ID: 34014827
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network.
Long Y; Luo J
BMC Bioinformatics; 2019 Nov; 20(1):541. PubMed ID: 31675979
[TBL] [Abstract][Full Text] [Related]
10. Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model.
Li H; Wang Y; Zhang Z; Tan Y; Chen Z; Wang X; Pei T; Wang L
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2502-2513. PubMed ID: 32305935
[TBL] [Abstract][Full Text] [Related]
11. Microbe-Disease Association Prediction Using RGCN Through Microbe-Drug-Disease Network.
Wang Y; Lei X; Pan Y
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3353-3362. PubMed ID: 37027603
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Novel human microbe-disease association prediction using network consistency projection.
Bao W; Jiang Z; Huang DS
BMC Bioinformatics; 2017 Dec; 18(Suppl 16):543. PubMed ID: 29297304
[TBL] [Abstract][Full Text] [Related]
14. A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations.
Li S; Xie M; Liu X
Front Genet; 2019; 10():1147. PubMed ID: 31803235
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier.
Ma Q; Tan Y; Wang L
BMC Bioinformatics; 2023 Feb; 24(1):35. PubMed ID: 36732704
[TBL] [Abstract][Full Text] [Related]
17. Predicting multiple types of MicroRNA-disease associations based on tensor factorization and label propagation.
Yu N; Liu ZP; Gao R
Comput Biol Med; 2022 Jul; 146():105558. PubMed ID: 35525071
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder.
Tan Y; Zou J; Kuang L; Wang X; Zeng B; Zhang Z; Wang L
BMC Bioinformatics; 2022 Nov; 23(1):492. PubMed ID: 36401174
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]