BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

120 related articles for article (PubMed ID: 26405960)

  • 1. Predicting a DNA-binding protein using random forest with multiple mathematical features.
    Guan C; Niu X; Shi F; Yang K; Li N
    Biomed Mater Eng; 2015; 26 Suppl 1():S1883-9. PubMed ID: 26405960
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.
    Nie G; Li Y; Wang F; Wang S; Hu X
    Biomed Mater Eng; 2015; 26 Suppl 1():S1829-36. PubMed ID: 26405954
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting DNA binding proteins using support vector machine with hybrid fractal features.
    Niu XH; Hu XH; Shi F; Xia JB
    J Theor Biol; 2014 Feb; 343():186-92. PubMed ID: 24189096
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.
    Park B; Im J; Tuvshinjargal N; Lee W; Han K
    Comput Methods Programs Biomed; 2014 Nov; 117(2):158-67. PubMed ID: 25113160
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Sequence-based prediction of DNA-binding residues in proteins with conservation and correlation information.
    Ma X; Guo J; Liu HD; Xie JM; Sun X
    IEEE/ACM Trans Comput Biol Bioinform; 2012; 9(6):1766-75. PubMed ID: 22868682
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Using the concept of Chou's pseudo amino acid composition to predict protein solubility: an approach with entropies in information theory.
    Xiaohui N; Nana L; Jingbo X; Dingyan C; Yuehua P; Yang X; Weiquan W; Dongming W; Zengzhen W
    J Theor Biol; 2013 Sep; 332():211-7. PubMed ID: 23524162
    [TBL] [Abstract][Full Text] [Related]  

  • 7. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor.
    Ma X; Guo J; Xiao K; Sun X
    IEEE/ACM Trans Comput Biol Bioinform; 2015; 12(6):1385-93. PubMed ID: 26671809
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting DNA- and RNA-binding proteins from sequences with kernel methods.
    Shao X; Tian Y; Wu L; Wang Y; Jing L; Deng N
    J Theor Biol; 2009 May; 258(2):289-93. PubMed ID: 19490865
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information.
    Ahmad S; Gromiha MM; Sarai A
    Bioinformatics; 2004 Mar; 20(4):477-86. PubMed ID: 14990443
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of DNA-binding residues from sequence features.
    Wang L; Brown SJ
    J Bioinform Comput Biol; 2006 Dec; 4(6):1141-58. PubMed ID: 17245807
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature.
    Ma X; Guo J; Wu J; Liu H; Yu J; Xie J; Sun X
    Proteins; 2011 Apr; 79(4):1230-9. PubMed ID: 21268114
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection.
    Damoulas T; Girolami MA
    Bioinformatics; 2008 May; 24(10):1264-70. PubMed ID: 18378524
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.
    Song J; Yuan Z; Tan H; Huber T; Burrage K
    Bioinformatics; 2007 Dec; 23(23):3147-54. PubMed ID: 17942444
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of DNA-binding residues from sequence.
    Ofran Y; Mysore V; Rost B
    Bioinformatics; 2007 Jul; 23(13):i347-53. PubMed ID: 17646316
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improved method for predicting beta-turn using support vector machine.
    Zhang Q; Yoon S; Welsh WJ
    Bioinformatics; 2005 May; 21(10):2370-4. PubMed ID: 15797917
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of protein structure classes with flexible neural tree.
    Bao W; Chen Y; Wang D
    Biomed Mater Eng; 2014; 24(6):3797-806. PubMed ID: 25227096
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.
    Wang L; Zhang W; Gao Q; Xiong C
    IET Syst Biol; 2014 Aug; 8(4):184-90. PubMed ID: 25075532
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins.
    Nanni L; Lumini A
    Amino Acids; 2009 Feb; 36(2):167-75. PubMed ID: 18288459
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EHPred: an SVM-based method for epoxide hydrolases recognition and classification.
    Jia J; Yang L; Zhang ZZ
    J Zhejiang Univ Sci B; 2006 Jan; 7(1):1-6. PubMed ID: 16365918
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DNA-Prot: identification of DNA binding proteins from protein sequence information using random forest.
    Kumar KK; Pugalenthi G; Suganthan PN
    J Biomol Struct Dyn; 2009 Jun; 26(6):679-86. PubMed ID: 19385697
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.