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 *

275 related articles for article (PubMed ID: 29532347)

  • 1. Prediction of Apoptosis Protein's Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information.
    Liang Y; Zhang S
    Acta Biotheor; 2018 Mar; 66(1):61-78. PubMed ID: 29532347
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.
    Liang Y; Liu S; Zhang S
    Math Biosci; 2016 Dec; 282():61-67. PubMed ID: 27720879
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm.
    Ruan X; Zhou D; Nie R; Hou R; Cao Z
    Med Biol Eng Comput; 2019 Dec; 57(12):2553-2565. PubMed ID: 31621050
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.
    Du L; Meng Q; Chen Y; Wu P
    BMC Bioinformatics; 2020 May; 21(1):212. PubMed ID: 32448129
    [TBL] [Abstract][Full Text] [Related]  

  • 6. iAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences.
    Tang Y; Xie L; Chen L
    Int J Mol Sci; 2018 Apr; 19(4):. PubMed ID: 29652843
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine.
    Xiang Q; Liao B; Li X; Xu H; Chen J; Shi Z; Dai Q; Yao Y
    Artif Intell Med; 2017 May; 78():41-46. PubMed ID: 28764871
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination.
    Liu T; Tao P; Li X; Qin Y; Wang C
    J Theor Biol; 2015 Feb; 366():8-12. PubMed ID: 25463695
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix.
    Ruan X; Zhou D; Nie R; Guo Y
    Biomed Res Int; 2020; 2020():4071508. PubMed ID: 32420339
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine.
    Zhou XB; Chen C; Li ZC; Zou XY
    Amino Acids; 2008 Aug; 35(2):383-8. PubMed ID: 18157588
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features.
    Li B; Cai L; Liao B; Fu X; Bing P; Yang J
    Molecules; 2019 Mar; 24(5):. PubMed ID: 30845684
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation.
    Liu T; Zheng X; Wang C; Wang J
    Protein Pept Lett; 2010 Oct; 17(10):1263-9. PubMed ID: 20670213
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine.
    Zhang ZH; Wang ZH; Zhang ZR; Wang YX
    FEBS Lett; 2006 Nov; 580(26):6169-74. PubMed ID: 17069811
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.
    Liang Y; Liu S; Zhang S
    Comput Math Methods Med; 2015; 2015():370756. PubMed ID: 26788119
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction.
    Yu B; Li S; Qiu W; Wang M; Du J; Zhang Y; Chen X
    BMC Genomics; 2018 Jun; 19(1):478. PubMed ID: 29914358
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Accurate prediction of Gram-negative bacterial secreted protein types by fusing multiple statistical features from PSI-BLAST profile.
    Liang Y; Zhang S; Ding S
    SAR QSAR Environ Res; 2018 Jun; 29(6):469-481. PubMed ID: 29688029
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A protein structural classes prediction method based on PSI-BLAST profile.
    Ding S; Yan S; Qi S; Li Y; Yao Y
    J Theor Biol; 2014 Jul; 353():19-23. PubMed ID: 24607742
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel representation for apoptosis protein subcellular localization prediction using support vector machine.
    Zhang L; Liao B; Li D; Zhu W
    J Theor Biol; 2009 Jul; 259(2):361-5. PubMed ID: 19328812
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.