164 related articles for article (PubMed ID: 25458812)
1. LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information.
Zahiri J; Mohammad-Noori M; Ebrahimpour R; Saadat S; Bozorgmehr JH; Goldberg T; Masoudi-Nejad A
Genomics; 2014 Dec; 104(6 Pt B):496-503. PubMed ID: 25458812
[TBL] [Abstract][Full Text] [Related]
2. Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.
Gao ZG; Wang L; Xia SX; You ZH; Yan X; Zhou Y
Biomed Res Int; 2016; 2016():4563524. PubMed ID: 27437399
[TBL] [Abstract][Full Text] [Related]
3. Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors.
Wong L; You ZH; Ming Z; Li J; Chen X; Huang YA
Int J Mol Sci; 2015 Dec; 17(1):. PubMed ID: 26712745
[TBL] [Abstract][Full Text] [Related]
4. Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.
You ZH; Chan KC; Hu P
PLoS One; 2015; 10(5):e0125811. PubMed ID: 25946106
[TBL] [Abstract][Full Text] [Related]
5. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier.
Chen C; Zhang Q; Yu B; Yu Z; Lawrence PJ; Ma Q; Zhang Y
Comput Biol Med; 2020 Aug; 123():103899. PubMed ID: 32768046
[TBL] [Abstract][Full Text] [Related]
6. Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction.
Ananthasubramanian S; Metri R; Khetan A; Gupta A; Handen A; Chandra N; Ganapathiraju M
Microb Inform Exp; 2012 Mar; 2():4. PubMed ID: 22587966
[TBL] [Abstract][Full Text] [Related]
7. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.
Zhao N; Han JG; Shyu CR; Korkin D
PLoS Comput Biol; 2014 May; 10(5):e1003592. PubMed ID: 24784581
[TBL] [Abstract][Full Text] [Related]
8. GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome.
Li F; Li C; Wang M; Webb GI; Zhang Y; Whisstock JC; Song J
Bioinformatics; 2015 May; 31(9):1411-9. PubMed ID: 25568279
[TBL] [Abstract][Full Text] [Related]
9. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines.
González AJ; Liao L
BMC Bioinformatics; 2010 Oct; 11():537. PubMed ID: 21034480
[TBL] [Abstract][Full Text] [Related]
10. Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier.
Wang L; You ZH; Xia SX; Liu F; Chen X; Yan X; Zhou Y
J Theor Biol; 2017 Apr; 418():105-110. PubMed ID: 28088356
[TBL] [Abstract][Full Text] [Related]
11. A novel feature extraction scheme with ensemble coding for protein-protein interaction prediction.
Du X; Cheng J; Zheng T; Duan Z; Qian F
Int J Mol Sci; 2014 Jul; 15(7):12731-49. PubMed ID: 25046746
[TBL] [Abstract][Full Text] [Related]
12. Improved Prediction of Protein-Protein Interaction Mapping on
Islam MM; Alam MJ; Ahmed FF; Hasan MM; Mollah MNH
Protein Pept Lett; 2021; 28(1):74-83. PubMed ID: 32520672
[TBL] [Abstract][Full Text] [Related]
13. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.
You ZH; Lei YK; Zhu L; Xia J; Wang B
BMC Bioinformatics; 2013; 14 Suppl 8(Suppl 8):S10. PubMed ID: 23815620
[TBL] [Abstract][Full Text] [Related]
14. A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.
Pan J; Wang S; Yu C; Li L; You Z; Sun Y
Biology (Basel); 2022 May; 11(5):. PubMed ID: 35625503
[TBL] [Abstract][Full Text] [Related]
15. SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.
Gupta P; Mohanty D
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33839740
[TBL] [Abstract][Full Text] [Related]
16. Machine-learning techniques for the prediction of protein-protein interactions.
Sarkar D; Saha S
J Biosci; 2019 Sep; 44(4):. PubMed ID: 31502581
[TBL] [Abstract][Full Text] [Related]
17. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.
Zhang M; Su Q; Lu Y; Zhao M; Niu B
Med Chem; 2017; 13(6):506-514. PubMed ID: 28530547
[TBL] [Abstract][Full Text] [Related]
18. Proteome-wide prediction and analysis of the
Ren P; Yang X; Wang T; Hou Y; Zhang Z
Comput Struct Biotechnol J; 2022; 20():2322-2331. PubMed ID: 35615014
[TBL] [Abstract][Full Text] [Related]
19. Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning.
Maheshwari S; Brylinski M
J Mol Recognit; 2015 Jan; 28(1):35-48. PubMed ID: 26268369
[TBL] [Abstract][Full Text] [Related]
20. InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes.
Sun J; Sun Y; Ding G; Liu Q; Wang C; He Y; Shi T; Li Y; Zhao Z
BMC Bioinformatics; 2007 Oct; 8():414. PubMed ID: 17963500
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]