110 related articles for article (PubMed ID: 37883850)
1. Prediction of submitochondrial proteins localization based on Gene Ontology.
Wang J; Zhou H; Wang Y; Xu M; Yu Y; Wang J; Liu Y
Comput Biol Med; 2023 Dec; 167():107589. PubMed ID: 37883850
[TBL] [Abstract][Full Text] [Related]
2. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting.
Yu B; Qiu W; Chen C; Ma A; Jiang J; Zhou H; Ma Q
Bioinformatics; 2020 Feb; 36(4):1074-1081. PubMed ID: 31603468
[TBL] [Abstract][Full Text] [Related]
3. Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization.
Mei S
J Theor Biol; 2012 Jan; 293():121-30. PubMed ID: 22037046
[TBL] [Abstract][Full Text] [Related]
4. DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment.
Wang X; Jin Y; Zhang Q
Int J Mol Sci; 2020 Aug; 21(16):. PubMed ID: 32784927
[TBL] [Abstract][Full Text] [Related]
5. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence.
Du P; Li Y
BMC Bioinformatics; 2006 Nov; 7():518. PubMed ID: 17134515
[TBL] [Abstract][Full Text] [Related]
6. Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction.
Li L; Yu S; Xiao W; Li Y; Hu W; Huang L; Zheng X; Zhou S; Yang H
Mol Biosyst; 2015 Jan; 11(1):170-7. PubMed ID: 25335193
[TBL] [Abstract][Full Text] [Related]
7. Protein Function Prediction With Functional and Topological Knowledge of Gene Ontology.
Zhao Y; Yang Z; Hong Y; Yang Y; Wang L; Zhang Y; Lin H; Wang J
IEEE Trans Nanobioscience; 2023 Oct; 22(4):755-762. PubMed ID: 37204950
[TBL] [Abstract][Full Text] [Related]
8. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.
Wan S; Mak MW; Kung SY
PLoS One; 2014; 9(3):e89545. PubMed ID: 24647341
[TBL] [Abstract][Full Text] [Related]
9. GO functional similarity clustering depends on similarity measure, clustering method, and annotation completeness.
Liu M; Thomas PD
BMC Bioinformatics; 2019 Mar; 20(1):155. PubMed ID: 30917779
[TBL] [Abstract][Full Text] [Related]
10. ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization.
Huang WL; Tung CW; Ho SW; Hwang SF; Ho SY
BMC Bioinformatics; 2008 Feb; 9():80. PubMed ID: 18241343
[TBL] [Abstract][Full Text] [Related]
11. Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.
Bouziane H; Chouarfia A
J Integr Bioinform; 2020 Jun; 18(1):51-79. PubMed ID: 32598314
[TBL] [Abstract][Full Text] [Related]
12. A weighted multipath measurement based on gene ontology for estimating gene products similarity.
Liu L; Dai X; Wang H; Song W; Lu J
J Comput Biol; 2014 Dec; 21(12):964-74. PubMed ID: 25229994
[TBL] [Abstract][Full Text] [Related]
13. Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito.
Savojardo C; Martelli PL; Tartari G; Casadio R
BMC Bioinformatics; 2020 Sep; 21(Suppl 8):266. PubMed ID: 32938368
[TBL] [Abstract][Full Text] [Related]
14. Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations.
Agapito G; Milano M; Guzzi PH; Cannataro M
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(2):197-208. PubMed ID: 27045823
[TBL] [Abstract][Full Text] [Related]
15. Gene ontology based transfer learning for protein subcellular localization.
Mei S; Fei W; Zhou S
BMC Bioinformatics; 2011 Feb; 12():44. PubMed ID: 21284890
[TBL] [Abstract][Full Text] [Related]
16. NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph.
Yu G; Fu G; Wang J; Zhao Y
IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(4):1390-1402. PubMed ID: 28641268
[TBL] [Abstract][Full Text] [Related]
17. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.
Peng J; Uygun S; Kim T; Wang Y; Rhee SY; Chen J
BMC Bioinformatics; 2015 Feb; 16():44. PubMed ID: 25886899
[TBL] [Abstract][Full Text] [Related]
18. A method for increasing expressivity of Gene Ontology annotations using a compositional approach.
Huntley RP; Harris MA; Alam-Faruque Y; Blake JA; Carbon S; Dietze H; Dimmer EC; Foulger RE; Hill DP; Khodiyar VK; Lock A; Lomax J; Lovering RC; Mutowo-Meullenet P; Sawford T; Van Auken K; Wood V; Mungall CJ
BMC Bioinformatics; 2014 May; 15():155. PubMed ID: 24885854
[TBL] [Abstract][Full Text] [Related]
19. mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines.
Wan S; Mak MW; Kung SY
BMC Bioinformatics; 2012 Nov; 13():290. PubMed ID: 23130999
[TBL] [Abstract][Full Text] [Related]
20. GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts.
Hinderer EW; Moseley HNB
PLoS One; 2020; 15(6):e0233311. PubMed ID: 32525872
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]