BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

136 related articles for article (PubMed ID: 38342046)

  • 1. PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning.
    Zhou Z; Xiao C; Yin J; She J; Duan H; Liu C; Fu X; Cui F; Qi Q; Zhang Z
    Comput Biol Med; 2024 Mar; 171():108129. PubMed ID: 38342046
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SNN6mA: Improved DNA N6-methyladenine site prediction using Siamese network-based feature embedding.
    Yu X; Hu J; Zhang Y
    Comput Biol Med; 2023 Nov; 166():107533. PubMed ID: 37793205
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.
    Hu J; Tang YX; Zhou Y; Li Z; Rao B; Zhang GJ
    J Chem Inf Model; 2023 Sep; 63(17):5689-5700. PubMed ID: 37603823
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome.
    Yu H; Dai Z
    Front Genet; 2019; 10():1071. PubMed ID: 31681441
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block.
    Liu M; Sun ZL; Zeng Z; Lam KM
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35325050
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites.
    Zhang Y; Liu Y; Xu J; Wang X; Peng X; Song J; Yu DJ
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34459479
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.
    Li Z; Jiang H; Kong L; Chen Y; Lang K; Fan X; Zhang L; Pian C
    PLoS Comput Biol; 2021 Feb; 17(2):e1008767. PubMed ID: 33600435
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Plant6mA: A predictor for predicting N6-methyladenine sites with lightweight structure in plant genomes.
    Shi H; Li S; Su X
    Methods; 2022 Aug; 204():126-131. PubMed ID: 35231584
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.
    Cai J; Wang D; Chen R; Niu Y; Ye X; Su R; Xiao G; Wei L
    Front Bioeng Biotechnol; 2020; 8():502. PubMed ID: 32582654
    [TBL] [Abstract][Full Text] [Related]  

  • 10. GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.
    Cai J; Xiao G; Su R
    Methods; 2022 Aug; 204():14-21. PubMed ID: 35149214
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction.
    Tsukiyama S; Hasan MM; Kurata H
    Comput Struct Biotechnol J; 2023; 21():644-654. PubMed ID: 36659917
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks.
    Abbas Z; Tayara H; Chong KT
    Int J Mol Sci; 2022 Jul; 23(15):. PubMed ID: 35955447
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning.
    Huang Q; Zhou W; Guo F; Xu L; Zhang L
    PeerJ; 2021; 9():e10813. PubMed ID: 33604189
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Critical evaluation of web-based DNA N6-methyladenine site prediction tools.
    Hasan MM; Shoombuatong W; Kurata H; Manavalan B
    Brief Funct Genomics; 2021 Jul; 20(4):258-272. PubMed ID: 33491072
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species.
    Tang X; Zheng P; Li X; Wu H; Wei DQ; Liu Y; Huang G
    Methods; 2022 Aug; 204():142-150. PubMed ID: 35477057
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.
    Huang G; Huang X; Luo W
    BioData Min; 2023 Nov; 16(1):34. PubMed ID: 38012796
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DNA N
    Karanthamalai J; Chodon A; Chauhan S; Pandi G
    Plants (Basel); 2020 Feb; 9(2):. PubMed ID: 32075056
    [TBL] [Abstract][Full Text] [Related]  

  • 18. i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting.
    Teng Z; Zhao Z; Li Y; Tian Z; Guo M; Lu Q; Wang G
    Front Plant Sci; 2022; 13():845835. PubMed ID: 35237293
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Ense-i6mA: Identification of DNA N6-methyl-adenine Sites Using XGB-RFE Feature Se-lection and Ensemble Machine Learning.
    Fan XQ; Lin B; Hu J; Guo ZY
    IEEE/ACM Trans Comput Biol Bioinform; 2024 Jul; PP():. PubMed ID: 38949938
    [TBL] [Abstract][Full Text] [Related]  

  • 20. i6mA-stack: A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome.
    Khanal J; Lim DY; Tayara H; Chong KT
    Genomics; 2021 Jan; 113(1 Pt 2):582-592. PubMed ID: 33010390
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.