BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

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]
    of 9.