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Journal Abstract Search

502 related items for PubMed ID: 32520672

  • 1. Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework.
    Islam MM, Alam MJ, Ahmed FF, Hasan MM, Mollah MNH.
    Protein Pept Lett; 2021; 28(1):74-83. PubMed ID: 32520672
    [Abstract] [Full Text] [Related]

  • 2. Prediction of Protein-Protein Interaction via co-occurring Aligned Pattern Clusters.
    Sze-To A, Fung S, Lee EA, Wong AKC.
    Methods; 2016 Nov 01; 110():26-34. PubMed ID: 27476008
    [Abstract] [Full Text] [Related]

  • 3. An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier.
    Tasmia SA, Ahmed FF, Mosharaf P, Hasan M, Mollah NH.
    Curr Genomics; 2021 Feb 01; 22(2):122-136. PubMed ID: 34220299
    [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 Feb 01; 10(5):e0125811. PubMed ID: 25946106
    [Abstract] [Full Text] [Related]

  • 5. Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest.
    Hou Q, De Geest PFG, Vranken WF, Heringa J, Feenstra KA.
    Bioinformatics; 2017 May 15; 33(10):1479-1487. PubMed ID: 28073761
    [Abstract] [Full Text] [Related]

  • 6. Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.
    Li Y, Wang Z, You ZH, Li LP, Hu X.
    Comput Math Methods Med; 2022 May 15; 2022():7191684. PubMed ID: 35242211
    [Abstract] [Full Text] [Related]

  • 7. SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.
    Li X, Han P, Wang G, Chen W, Wang S, Song T.
    BMC Genomics; 2022 Jun 27; 23(1):474. PubMed ID: 35761175
    [Abstract] [Full Text] [Related]

  • 8. UbNiRF: A Hybrid Framework Based on Null Importances and Random Forest that Combines Multiple Features to Predict Ubiquitination Sites in Arabidopsis thaliana and Homo sapiens.
    Li X, Yuan Z, Chen Y.
    Front Biosci (Landmark Ed); 2024 May 21; 29(5):197. PubMed ID: 38812315
    [Abstract] [Full Text] [Related]

  • 9. 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 May 21; 14 Suppl 8(Suppl 8):S10. PubMed ID: 23815620
    [Abstract] [Full Text] [Related]

  • 10. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.
    Sriwastava BK, Basu S, Maulik U.
    J Biosci; 2015 Oct 21; 40(4):809-18. PubMed ID: 26564981
    [Abstract] [Full Text] [Related]

  • 11. Machine-learning techniques for the prediction of protein-protein interactions.
    Sarkar D, Saha S.
    J Biosci; 2019 Sep 21; 44(4):. PubMed ID: 31502581
    [Abstract] [Full Text] [Related]

  • 12. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.
    An JY, You ZH, Meng FR, Xu SJ, Wang Y.
    Int J Mol Sci; 2016 May 18; 17(5):. PubMed ID: 27213337
    [Abstract] [Full Text] [Related]

  • 13. Protein features fusion using attributed network embedding for predicting protein-protein interaction.
    Cao MY, Zainudin S, Daud KM.
    BMC Genomics; 2024 May 13; 25(1):466. PubMed ID: 38741045
    [Abstract] [Full Text] [Related]

  • 14. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.
    Su EC, Chang JM, Cheng CW, Sung TY, Hsu WL.
    BMC Bioinformatics; 2012 May 13; 13 Suppl 17(Suppl 17):S13. PubMed ID: 23282098
    [Abstract] [Full Text] [Related]

  • 15. Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.
    Tasmia SA, Kibria MK, Tuly KF, Islam MA, Khatun MS, Hasan MM, Mollah MNH.
    Sci Rep; 2022 Feb 16; 12(1):2632. PubMed ID: 35173235
    [Abstract] [Full Text] [Related]

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  • 19. ANOVA-particle swarm optimization-based feature selection and gradient boosting machine classifier for improved protein-protein interaction prediction.
    Mahapatra S, Sahu SS.
    Proteins; 2022 Feb 16; 90(2):443-454. PubMed ID: 34528291
    [Abstract] [Full Text] [Related]

  • 20. A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction.
    Bandyopadhyay S, Mallick K.
    IEEE/ACM Trans Comput Biol Bioinform; 2017 Feb 16; 14(4):762-770. PubMed ID: 28113911
    [Abstract] [Full Text] [Related]

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