BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 31310738)

  • 1. Identification of amyloidogenic peptides via optimized integrated features space based on physicochemical properties and PSSM.
    Zhou C; Liu S; Zhang S
    Anal Biochem; 2019 Oct; 583():113362. PubMed ID: 31310738
    [TBL] [Abstract][Full Text] [Related]  

  • 2. RFAmyloid: A Web Server for Predicting Amyloid Proteins.
    Niu M; Li Y; Wang C; Han K
    Int J Mol Sci; 2018 Jul; 19(7):. PubMed ID: 30013015
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of lipid-binding sites based on support vector machine and position specific scoring matrix.
    Xiong W; Guo Y; Li M
    Protein J; 2010 Aug; 29(6):427-31. PubMed ID: 20658312
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.
    Nair SS; Subba Reddy NV; Hareesha KS
    BMC Bioinformatics; 2011; 12 Suppl 13(Suppl 13):S21. PubMed ID: 22373069
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of oxidoreductase subfamily classes based on RFE-SND-CC-PSSM and machine learning methods.
    Yuan F; Liu G; Yang X; Wang S; Wang X
    J Bioinform Comput Biol; 2019 Aug; 17(4):1950029. PubMed ID: 31617464
    [TBL] [Abstract][Full Text] [Related]  

  • 6. FISH Amyloid - a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of aminoacids.
    Gasior P; Kotulska M
    BMC Bioinformatics; 2014 Feb; 15():54. PubMed ID: 24564523
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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; 17(5):. PubMed ID: 27213337
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction.
    Wang Y; Ding Y; Tang J; Dai Y; Guo F
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(1):325-335. PubMed ID: 31027046
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier.
    Yang Z; Wang J; Zheng Z; Bai X
    Molecules; 2018 Aug; 23(8):. PubMed ID: 30103521
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.
    Zhang S; Liang Y
    J Theor Biol; 2018 Nov; 457():163-169. PubMed ID: 30179589
    [TBL] [Abstract][Full Text] [Related]  

  • 11. DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.
    Ali F; Ahmed S; Swati ZNK; Akbar S
    J Comput Aided Mol Des; 2019 Jul; 33(7):645-658. PubMed ID: 31123959
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.
    Li X; Liu T; Tao P; Wang C; Chen L
    Comput Biol Chem; 2015 Dec; 59 Pt A():95-100. PubMed ID: 26460680
    [TBL] [Abstract][Full Text] [Related]  

  • 14. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.
    Wang Y; You Z; Li X; Chen X; Jiang T; Zhang J
    Int J Mol Sci; 2017 May; 18(5):. PubMed ID: 28492483
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting.
    Ali F; Kumar H; Patil S; Kotecha K; Banjar A; Daud A
    Comput Biol Med; 2022 Jun; 145():105533. PubMed ID: 35447463
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties.
    Pan G; Jiang L; Tang J; Guo F
    Int J Mol Sci; 2018 Feb; 19(2):. PubMed ID: 29419752
    [TBL] [Abstract][Full Text] [Related]  

  • 17. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.
    Charoenkwan P; Ahmed S; Nantasenamat C; Quinn JMW; Moni MA; Lio' P; Shoombuatong W
    Sci Rep; 2022 May; 12(1):7697. PubMed ID: 35546347
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information.
    Ma X; Guo J; Sun X
    J Bioinform Comput Biol; 2018 Jun; 16(3):1840009. PubMed ID: 29591488
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties.
    Huang HL; Lin IC; Liou YF; Tsai CT; Hsu KT; Huang WL; Ho SJ; Ho SY
    BMC Bioinformatics; 2011 Feb; 12 Suppl 1(Suppl 1):S47. PubMed ID: 21342579
    [TBL] [Abstract][Full Text] [Related]  

  • 20. FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence.
    Garbuzynskiy SO; Lobanov MY; Galzitskaya OV
    Bioinformatics; 2010 Feb; 26(3):326-32. PubMed ID: 20019059
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.