BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

157 related articles for article (PubMed ID: 30880183)

  • 1. dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components.
    Ning Q; Ma Z; Zhao X
    J Theor Biol; 2019 Jun; 470():43-49. PubMed ID: 30880183
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components.
    Ju Z; Wang SY
    Genomics; 2020 Jan; 112(1):859-866. PubMed ID: 31175975
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling.
    Jia C; Zhang M; Fan C; Li F; Song J
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):1937-1945. PubMed ID: 31804942
    [TBL] [Abstract][Full Text] [Related]  

  • 4. pQLyCar: Peptide-based dynamic query-driven sample rescaling strategy for identifying carboxylation sites combined with KNN and SVM.
    Ning Q; Deng A; Zou T; Zhao X
    Anal Biochem; 2021 Nov; 633():114386. PubMed ID: 34543644
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques.
    Sohrawordi M; Hossain MA
    Biochimie; 2022 Jan; 192():125-135. PubMed ID: 34627982
    [TBL] [Abstract][Full Text] [Related]  

  • 6. iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule.
    Malebary SJ; Rehman MSU; Khan YD
    PLoS One; 2019; 14(11):e0223993. PubMed ID: 31751380
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC.
    Ju Z; He JJ
    J Mol Graph Model; 2017 Oct; 77():200-204. PubMed ID: 28886434
    [TBL] [Abstract][Full Text] [Related]  

  • 8. predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance.
    Islam MKB; Rahman J; Hasan MAM; Ahmad S
    Comput Biol Chem; 2021 Oct; 94():107553. PubMed ID: 34384997
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of protein N-formylation using the composition of k-spaced amino acid pairs.
    Ju Z; Cao JZ
    Anal Biochem; 2017 Oct; 534():40-45. PubMed ID: 28709899
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net.
    Liu Y; Yu Z; Chen C; Han Y; Yu B
    Anal Biochem; 2020 Nov; 609():113903. PubMed ID: 32805274
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of carbamylated lysine sites based on the one-class k-nearest neighbor method.
    Huang G; Zhou Y; Zhang Y; Li BQ; Zhang N; Cai YD
    Mol Biosyst; 2013 Nov; 9(11):2729-40. PubMed ID: 24056952
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy.
    Jiang X; Wei R; Zhang T; Gu Q
    Protein Pept Lett; 2008; 15(4):392-6. PubMed ID: 18473953
    [TBL] [Abstract][Full Text] [Related]  

  • 14. FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.
    Ning Q; Qi Z; Wang Y; Deng A; Chen C
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36168700
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Phogly-PseAAC: Prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity.
    Xu Y; Ding YX; Ding J; Wu LY; Deng NY
    J Theor Biol; 2015 Aug; 379():10-5. PubMed ID: 25913879
    [TBL] [Abstract][Full Text] [Related]  

  • 16. iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.
    Ju Z; Cao JZ; Gu H
    J Theor Biol; 2015 Nov; 385():50-7. PubMed ID: 26254214
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou's PseAAC.
    Hayat M; Khan A
    Protein Pept Lett; 2012 Apr; 19(4):411-21. PubMed ID: 22185508
    [TBL] [Abstract][Full Text] [Related]  

  • 18. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier.
    Qiu WR; Sun BQ; Xiao X; Xu ZC; Jia JH; Chou KC
    Genomics; 2018 Sep; 110(5):239-246. PubMed ID: 29107015
    [TBL] [Abstract][Full Text] [Related]  

  • 19. iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC
    Ilyas S; Hussain W; Ashraf A; Khan YD; Khan SA; Chou KC
    Curr Genomics; 2019 May; 20(4):275-292. PubMed ID: 32030087
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components.
    Ahmad J; Hayat M
    J Theor Biol; 2019 Feb; 463():99-109. PubMed ID: 30562500
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.