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 *

195 related articles for article (PubMed ID: 33340540)

  • 1. Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations.
    Naseer S; Hussain W; Khan YD; Rasool N
    Anal Biochem; 2021 Feb; 615():114069. PubMed ID: 33340540
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions.
    Naseer S; Hussain W; Khan YD; Rasool N
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(3):1703-1714. PubMed ID: 33242308
    [TBL] [Abstract][Full Text] [Related]  

  • 3. DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information.
    Ahmed S; Kabir M; Arif M; Khan ZU; Yu DJ
    Anal Biochem; 2021 Jan; 612():113955. PubMed ID: 32949607
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network.
    Khalili E; Ramazi S; Ghanati F; Kouchaki S
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35152280
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites.
    Long H; Liao B; Xu X; Yang J
    Int J Mol Sci; 2018 Sep; 19(9):. PubMed ID: 30231550
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Boosting phosphorylation site prediction with sequence feature-based machine learning.
    Maiti S; Hassan A; Mitra P
    Proteins; 2020 Feb; 88(2):284-291. PubMed ID: 31412138
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mini-review: Recent advances in post-translational modification site prediction based on deep learning.
    Meng L; Chan WS; Huang L; Liu L; Chen X; Zhang W; Wang F; Cheng K; Sun H; Wong KC
    Comput Struct Biotechnol J; 2022; 20():3522-3532. PubMed ID: 35860402
    [TBL] [Abstract][Full Text] [Related]  

  • 8. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions.
    Naseer S; Ali RF; Khan YD; Dominic PDD
    J Biomol Struct Dyn; 2022; 40(22):11691-11704. PubMed ID: 34396935
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.
    Chen Z; Liu X; Li F; Li C; Marquez-Lago T; Leier A; Akutsu T; Webb GI; Xu D; Smith AI; Li L; Chou KC; Song J
    Brief Bioinform; 2019 Nov; 20(6):2267-2290. PubMed ID: 30285084
    [TBL] [Abstract][Full Text] [Related]  

  • 10. iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.
    Khan YD; Rasool N; Hussain W; Khan SA; Chou KC
    Mol Biol Rep; 2018 Dec; 45(6):2501-2509. PubMed ID: 30311130
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.
    Khan S; AlQahtani SA; Noor S; Ahmad N
    BMC Bioinformatics; 2024 Aug; 25(1):284. PubMed ID: 39215231
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment.
    Akmal MA; Hussain W; Rasool N; Khan YD; Khan SA; Chou KC
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):2045-2056. PubMed ID: 31985438
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.
    Tasmia SA; Kibria MK; Tuly KF; Islam MA; Khatun MS; Hasan MM; Mollah MNH
    Sci Rep; 2022 Feb; 12(1):2632. PubMed ID: 35173235
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.
    Naseer S; Ali RF; Fati SM; Muneer A
    Sci Rep; 2022 Jan; 12(1):128. PubMed ID: 34996975
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.
    Liu Y; Wang M; Xi J; Luo F; Li A
    Int J Biol Sci; 2018; 14(8):946-956. PubMed ID: 29989096
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning.
    Xie Y; Luo X; Li Y; Chen L; Ma W; Huang J; Cui J; Zhao Y; Xue Y; Zuo Z; Ren J
    Genomics Proteomics Bioinformatics; 2018 Aug; 16(4):294-306. PubMed ID: 30268931
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices.
    Tatjewski M; Kierczak M; Plewczynski D
    Methods Mol Biol; 2017; 1484():275-300. PubMed ID: 27787833
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Accurately predicting microbial phosphorylation sites using evolutionary and structural features.
    Ahmed F; Dehzangi I; Hasan MM; Shatabda S
    Gene; 2023 Jan; 851():146993. PubMed ID: 36272653
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.
    Esmaili F; Pourmirzaei M; Ramazi S; Shojaeilangari S; Yavari E
    Genomics Proteomics Bioinformatics; 2023 Dec; 21(6):1266-1285. PubMed ID: 37863385
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.