BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

284 related articles for article (PubMed ID: 24577312)

  • 1. PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.
    Du P; Gu S; Jiao Y
    Int J Mol Sci; 2014 Feb; 15(3):3495-506. PubMed ID: 24577312
    [TBL] [Abstract][Full Text] [Related]  

  • 2. PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions.
    Du P; Wang X; Xu C; Gao Y
    Anal Biochem; 2012 Jun; 425(2):117-9. PubMed ID: 22459120
    [TBL] [Abstract][Full Text] [Related]  

  • 3. propy: a tool to generate various modes of Chou's PseAAC.
    Cao DS; Xu QS; Liang YZ
    Bioinformatics; 2013 Apr; 29(7):960-2. PubMed ID: 23426256
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A new signal characterization and signal-based Chou's PseAAC representation of protein sequences.
    Sanchez V; Peinado AM; Pérez-Córdoba JL; Gómez AM
    J Bioinform Comput Biol; 2015 Oct; 13(5):1550024. PubMed ID: 26434573
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identify DNA-binding proteins with optimal Chou's amino acid composition.
    Zhao XW; Li XT; Ma ZQ; Yin MH
    Protein Pept Lett; 2012 Apr; 19(4):398-405. PubMed ID: 22316304
    [TBL] [Abstract][Full Text] [Related]  

  • 6. DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.
    Rahman MS; Shatabda S; Saha S; Kaykobad M; Rahman MS
    J Theor Biol; 2018 Sep; 452():22-34. PubMed ID: 29753757
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection.
    Jiao YS; Du PF
    J Theor Biol; 2016 Aug; 402():38-44. PubMed ID: 27155042
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using a novel AdaBoost algorithm and Chou's Pseudo amino acid composition for predicting protein subcellular localization.
    Lin J; Wang Y
    Protein Pept Lett; 2011 Dec; 18(12):1219-25. PubMed ID: 21728988
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computational prediction of antifungal peptides via Chou's PseAAC and SVM.
    Mousavizadegan M; Mohabatkar H
    J Bioinform Comput Biol; 2018 Aug; 16(4):1850016. PubMed ID: 30105927
    [TBL] [Abstract][Full Text] [Related]  

  • 10. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset.
    Xiao X; Cheng X; Chen G; Mao Q; Chou KC
    Med Chem; 2019; 15(5):496-509. PubMed ID: 30556503
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Incorporating secondary features into the general form of Chou's PseAAC for predicting protein structural class.
    Liao B; Xiang Q; Li D
    Protein Pept Lett; 2012 Nov; 19(11):1133-8. PubMed ID: 22185510
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou's pseudo amino acid composition.
    Chen C; Shen ZB; Zou XY
    Protein Pept Lett; 2012 Apr; 19(4):422-9. PubMed ID: 22185506
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.
    Contreras-Torres E
    J Theor Biol; 2018 Oct; 454():139-145. PubMed ID: 29870696
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting antibacterial peptides by the concept of Chou's pseudo-amino acid composition and machine learning methods.
    Khosravian M; Faramarzi FK; Beigi MM; Behbahani M; Mohabatkar H
    Protein Pept Lett; 2013 Feb; 20(2):180-6. PubMed ID: 22894156
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.
    Qin YF; Wang CH; Yu XQ; Zhu J; Liu TG; Zheng XQ
    Protein Pept Lett; 2012 Apr; 19(4):388-97. PubMed ID: 22316305
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using Chou's pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach.
    Zhang SW; Chen W; Yang F; Pan Q
    Amino Acids; 2008 Oct; 35(3):591-8. PubMed ID: 18427713
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC.
    Han GS; Yu ZG; Anh V
    J Theor Biol; 2014 Mar; 344():31-9. PubMed ID: 24316387
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition.
    Liang Y; Zhang S
    J Mol Graph Model; 2017 Nov; 78():110-117. PubMed ID: 29055184
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.
    Hussain W; Khan YD; Rasool N; Khan SA; Chou KC
    Anal Biochem; 2019 Mar; 568():14-23. PubMed ID: 30593778
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC.
    Zhang S; Duan X
    J Theor Biol; 2018 Jan; 437():239-250. PubMed ID: 29100918
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.