BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

325 related articles for article (PubMed ID: 30452960)

  • 61. CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition.
    Khan A; Majid A; Hayat M
    Comput Biol Chem; 2011 Aug; 35(4):218-29. PubMed ID: 21864791
    [TBL] [Abstract][Full Text] [Related]  

  • 62. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.
    Wang J; Zhang L; Jia L; Ren Y; Yu G
    Int J Mol Sci; 2017 Nov; 18(11):. PubMed ID: 29117139
    [TBL] [Abstract][Full Text] [Related]  

  • 63. Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier.
    Zhang Q; Zhang Y; Li S; Han Y; Jin S; Gu H; Yu B
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33537726
    [TBL] [Abstract][Full Text] [Related]  

  • 64. Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC.
    Zhai JX; Cao TJ; An JY; Bian YT
    J Theor Biol; 2017 Nov; 432():80-86. PubMed ID: 28802824
    [TBL] [Abstract][Full Text] [Related]  

  • 65. Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.
    An JY; You ZH; Chen X; Huang DS; Yan G; Wang DF
    Mol Biosyst; 2016 Nov; 12(12):3702-3710. PubMed ID: 27759121
    [TBL] [Abstract][Full Text] [Related]  

  • 66. 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]  

  • 67. Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.
    Liu B; Chen J; Wang X
    Mol Genet Genomics; 2015 Oct; 290(5):1919-31. PubMed ID: 25896721
    [TBL] [Abstract][Full Text] [Related]  

  • 68. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines.
    González AJ; Liao L
    BMC Bioinformatics; 2010 Oct; 11():537. PubMed ID: 21034480
    [TBL] [Abstract][Full Text] [Related]  

  • 69. Amalgamation of 3D structure and sequence information for protein-protein interaction prediction.
    Jha K; Saha S
    Sci Rep; 2020 Nov; 10(1):19171. PubMed ID: 33154416
    [TBL] [Abstract][Full Text] [Related]  

  • 70. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.
    Zhang YN; Pan XY; Huang Y; Shen HB
    J Theor Biol; 2011 Aug; 283(1):44-52. PubMed ID: 21635901
    [TBL] [Abstract][Full Text] [Related]  

  • 71. An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences.
    Wang L; You ZH; Chen X; Li JQ; Yan X; Zhang W; Huang YA
    Oncotarget; 2017 Jan; 8(3):5149-5159. PubMed ID: 28029645
    [TBL] [Abstract][Full Text] [Related]  

  • 72. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.
    Ju Z; He JJ
    J Mol Graph Model; 2017 Sep; 76():356-363. PubMed ID: 28763688
    [TBL] [Abstract][Full Text] [Related]  

  • 73. 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]  

  • 74. Prediction of Protein-Protein Interactions by Evidence Combining Methods.
    Chang JW; Zhou YQ; Ul Qamar MT; Chen LL; Ding YD
    Int J Mol Sci; 2016 Nov; 17(11):. PubMed ID: 27879651
    [TBL] [Abstract][Full Text] [Related]  

  • 75. 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]  

  • 76. Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform.
    Qiu JD; Huang JH; Shi SP; Liang RP
    Protein Pept Lett; 2010 Jun; 17(6):715-22. PubMed ID: 19961429
    [TBL] [Abstract][Full Text] [Related]  

  • 77. Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine.
    Kang C; Huo Y; Xin L; Tian B; Yu B
    J Theor Biol; 2019 Feb; 463():77-91. PubMed ID: 30537483
    [TBL] [Abstract][Full Text] [Related]  

  • 78. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting.
    Yu B; Qiu W; Chen C; Ma A; Jiang J; Zhou H; Ma Q
    Bioinformatics; 2020 Feb; 36(4):1074-1081. PubMed ID: 31603468
    [TBL] [Abstract][Full Text] [Related]  

  • 79. Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine.
    Mohabatkar H; Mohammad Beigi M; Esmaeili A
    J Theor Biol; 2011 Jul; 281(1):18-23. PubMed ID: 21536049
    [TBL] [Abstract][Full Text] [Related]  

  • 80. Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection.
    Liu L; Cai Y; Lu W; Feng K; Peng C; Niu B
    Biochem Biophys Res Commun; 2009 Mar; 380(2):318-22. PubMed ID: 19171120
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 17.