These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

291 related articles for article (PubMed ID: 23504705)

  • 1. Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.
    Chen P; Li J; Wong L; Kuwahara H; Huang JZ; Gao X
    Proteins; 2013 Aug; 81(8):1351-62. PubMed ID: 23504705
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Protein binding hot spots prediction from sequence only by a new ensemble learning method.
    Hu SS; Chen P; Wang B; Li J
    Amino Acids; 2017 Oct; 49(10):1773-1785. PubMed ID: 28766075
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of hot spots in protein interfaces using a random forest model with hybrid features.
    Wang L; Liu ZP; Zhang XS; Chen L
    Protein Eng Des Sel; 2012 Mar; 25(3):119-26. PubMed ID: 22258275
    [TBL] [Abstract][Full Text] [Related]  

  • 4. APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility.
    Xia JF; Zhao XM; Song J; Huang DS
    BMC Bioinformatics; 2010 Apr; 11():174. PubMed ID: 20377884
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.
    Xia J; Yue Z; Di Y; Zhu X; Zheng CH
    Oncotarget; 2016 Apr; 7(14):18065-75. PubMed ID: 26934646
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy.
    Tuncbag N; Gursoy A; Keskin O
    Bioinformatics; 2009 Jun; 25(12):1513-20. PubMed ID: 19357097
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A feature-based approach to predict hot spots in protein-DNA binding interfaces.
    Zhang S; Zhao L; Zheng CH; Xia J
    Brief Bioinform; 2020 May; 21(3):1038-1046. PubMed ID: 30957840
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.
    Barenboim M; Masso M; Vaisman II; Jamison DC
    Proteins; 2008 Jun; 71(4):1930-9. PubMed ID: 18186470
    [TBL] [Abstract][Full Text] [Related]  

  • 9. IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids.
    Ali S; Majid A; Khan A
    Amino Acids; 2014 Apr; 46(4):977-93. PubMed ID: 24390396
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Geometrically centered region: a "wet" model of protein binding hot spots not excluding water molecules.
    Li Z; Li J
    Proteins; 2010 Dec; 78(16):3304-16. PubMed ID: 20818601
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.
    Jiang J; Wang N; Chen P; Zheng C; Wang B
    Int J Mol Sci; 2017 Jul; 18(7):. PubMed ID: 28718782
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy.
    Qiao Y; Xiong Y; Gao H; Zhu X; Chen P
    BMC Bioinformatics; 2018 Jan; 19(1):14. PubMed ID: 29334889
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach.
    Pan Y; Zhou S; Guan J
    BMC Bioinformatics; 2020 Sep; 21(Suppl 13):384. PubMed ID: 32938375
    [TBL] [Abstract][Full Text] [Related]  

  • 14. KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features.
    Zhu X; Mitchell JC
    Proteins; 2011 Sep; 79(9):2671-83. PubMed ID: 21735484
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Hot spot prediction in protein-protein interactions by an ensemble system.
    Liu Q; Chen P; Wang B; Zhang J; Li J
    BMC Syst Biol; 2018 Dec; 12(Suppl 9):132. PubMed ID: 30598091
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.
    Zhang S; Wang L; Zhao L; Li M; Liu M; Li K; Bin Y; Xia J
    BMC Bioinformatics; 2021 May; 22(Suppl 3):253. PubMed ID: 34000983
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.
    Pan Y; Wang Z; Zhan W; Deng L
    Bioinformatics; 2018 May; 34(9):1473-1480. PubMed ID: 29281004
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.
    Zahiri J; Yaghoubi O; Mohammad-Noori M; Ebrahimpour R; Masoudi-Nejad A
    Genomics; 2013 Oct; 102(4):237-42. PubMed ID: 23747746
    [TBL] [Abstract][Full Text] [Related]  

  • 19. AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices.
    Tantoso E; Li KB
    Amino Acids; 2008 Aug; 35(2):345-53. PubMed ID: 18163182
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.
    Shamim MT; Anwaruddin M; Nagarajaram HA
    Bioinformatics; 2007 Dec; 23(24):3320-7. PubMed ID: 17989092
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.