260 related articles for article (PubMed ID: 31542696)
21. i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.
Xue T; Zhang S; Qiao H
Interdiscip Sci; 2021 Sep; 13(3):413-425. PubMed ID: 33834381
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. N
Zhang Q; Liang Z; Cui X; Ji C; Li Y; Zhang P; Liu J; Riaz A; Yao P; Liu M; Wang Y; Lu T; Yu H; Yang D; Zheng H; Gu X
Mol Plant; 2018 Dec; 11(12):1492-1508. PubMed ID: 30448535
[TBL] [Abstract][Full Text] [Related]
24. Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
Basith S; Hasan MM; Lee G; Wei L; Manavalan B
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34226917
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.
Rahman CR; Amin R; Shatabda S; Toaha MSI
Sci Rep; 2021 May; 11(1):10357. PubMed ID: 33990665
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. 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]
29. MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model.
Pian C; Zhang G; Li F; Fan X
Bioinformatics; 2020 Jan; 36(2):388-392. PubMed ID: 31297537
[TBL] [Abstract][Full Text] [Related]
30. DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.
Manavalan B; Shin TH; Lee G
Oncotarget; 2018 Jan; 9(2):1944-1956. PubMed ID: 29416743
[TBL] [Abstract][Full Text] [Related]
31. Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.
Manavalan B; Basith S; Shin TH; Lee G
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33232970
[TBL] [Abstract][Full Text] [Related]
32. 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]
33. 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]
34. DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.
Nabeel Asim M; Ali Ibrahim M; Fazeel A; Dengel A; Ahmed S
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36528802
[TBL] [Abstract][Full Text] [Related]
35. 6mA DNA Methylation on Genes in Plants Is Associated with Gene Complexity, Expression and Duplication.
Zhang Y; Zhang Q; Yang X; Gu X; Chen J; Shi T
Plants (Basel); 2023 May; 12(10):. PubMed ID: 37653866
[No Abstract] [Full Text] [Related]
36. SoftVoting6mA: An improved ensemble-based method for predicting DNA N6-methyladenine sites in cross-species genomes.
Yin Z; Lyu J; Zhang G; Huang X; Ma Q; Jiang J
Math Biosci Eng; 2024 Feb; 21(3):3798-3815. PubMed ID: 38549308
[TBL] [Abstract][Full Text] [Related]
37. EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species.
Li F; Liu S; Li K; Zhang Y; Duan M; Yao Z; Zhu G; Guo Y; Wang Y; Huang L; Zhou F
Comput Biol Med; 2023 Jun; 160():107030. PubMed ID: 37196456
[TBL] [Abstract][Full Text] [Related]
38. DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool.
Rehman MU; Chong KT
Genes (Basel); 2020 Aug; 11(8):. PubMed ID: 32764497
[TBL] [Abstract][Full Text] [Related]
39. AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.
Manavalan B; Basith S; Shin TH; Wei L; Lee G
Comput Struct Biotechnol J; 2019; 17():972-981. PubMed ID: 31372196
[No Abstract] [Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]