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 *

166 related articles for article (PubMed ID: 36320563)

  • 1. DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.
    Suleman MT; Alkhalifah T; Alturise F; Khan YD
    PeerJ; 2022; 10():e14104. PubMed ID: 36320563
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach.
    Harun-Or-Roshid M; Maeda K; Phan LT; Manavalan B; Kurata H
    Comput Biol Med; 2024 Feb; 169():107848. PubMed ID: 38145601
    [TBL] [Abstract][Full Text] [Related]  

  • 3. iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models.
    Suleman MT; Alturise F; Alkhalifah T; Khan YD
    Digit Health; 2023; 9():20552076231165963. PubMed ID: 37009307
    [TBL] [Abstract][Full Text] [Related]  

  • 4. m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence.
    Suleman MT; Khan YD
    Comb Chem High Throughput Screen; 2022; 25(14):2473-2484. PubMed ID: 35718969
    [TBL] [Abstract][Full Text] [Related]  

  • 5. iRNAD: a computational tool for identifying D modification sites in RNA sequence.
    Xu ZC; Feng PM; Yang H; Qiu WR; Chen W; Lin H
    Bioinformatics; 2019 Dec; 35(23):4922-4929. PubMed ID: 31077296
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.
    Zhu H; Ao CY; Ding YJ; Hao HX; Yu L
    Int J Mol Sci; 2022 Mar; 23(6):. PubMed ID: 35328461
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identification of D Modification Sites by Integrating Heterogeneous Features in
    Feng P; Xu Z; Yang H; Lv H; Ding H; Liu L
    Molecules; 2019 Jan; 24(3):. PubMed ID: 30678171
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.
    Chen Z; Zhao P; Li F; Wang Y; Smith AI; Webb GI; Akutsu T; Baggag A; Bensmail H; Song J
    Brief Bioinform; 2020 Sep; 21(5):1676-1696. PubMed ID: 31714956
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Accurate identification of RNA D modification using multiple features.
    Dou L; Zhou W; Zhang L; Xu L; Han K
    RNA Biol; 2021 Dec; 18(12):2236-2246. PubMed ID: 33729104
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs.
    Wang Y; Wang X; Cui X; Meng J; Rong R
    Mol Ther Nucleic Acids; 2023 Mar; 31():411-420. PubMed ID: 36845339
    [TBL] [Abstract][Full Text] [Related]  

  • 11. NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.
    Ao C; Zou Q; Yu L
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34850821
    [TBL] [Abstract][Full Text] [Related]  

  • 12. PseU-Pred: An ensemble model for accurate identification of pseudouridine sites.
    Suleman MT; Khan YD
    Anal Biochem; 2023 Sep; 676():115247. PubMed ID: 37437648
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
    Hasan MM; Basith S; Khatun MS; Lee G; Manavalan B; Kurata H
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32910169
    [TBL] [Abstract][Full Text] [Related]  

  • 14. iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features.
    Huang KY; Hung FY; Kao HJ; Lau HH; Weng SL
    BMC Bioinformatics; 2020 Dec; 21(1):568. PubMed ID: 33297954
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Pretoria: An effective computational approach for accurate and high-throughput identification of CD8
    Charoenkwan P; Schaduangrat N; Pham NT; Manavalan B; Shoombuatong W
    Int J Biol Macromol; 2023 May; 238():124228. PubMed ID: 36996953
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.
    Harun-Or-Roshid M; Pham NT; Manavalan B; Kurata H
    PLoS One; 2024; 19(6):e0305406. PubMed ID: 38924058
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PseUI: Pseudouridine sites identification based on RNA sequence information.
    He J; Fang T; Zhang Z; Huang B; Zhu X; Xiong Y
    BMC Bioinformatics; 2018 Aug; 19(1):306. PubMed ID: 30157750
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Molecular evolution of dihydrouridine synthases.
    Kasprzak JM; Czerwoniec A; Bujnicki JM
    BMC Bioinformatics; 2012 Jun; 13():153. PubMed ID: 22741570
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.
    Wei L; Tang J; Zou Q
    BMC Genomics; 2017 Oct; 18(Suppl 7):742. PubMed ID: 29513192
    [TBL] [Abstract][Full Text] [Related]  

  • 20. iAcety-SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest.
    Malebary S; Rahman S; Barukab O; Ash'ari R; Khan SA
    Membranes (Basel); 2022 Feb; 12(3):. PubMed ID: 35323738
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.