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.
150 related articles for article (PubMed ID: 33119044)
1. iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor. Cai L; Ren X; Fu X; Peng L; Gao M; Zeng X Bioinformatics; 2021 May; 37(8):1060-1067. PubMed ID: 33119044 [TBL] [Abstract][Full Text] [Related]
2. iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using sequence information. Wu H; Liu M; Zhang P; Zhang H Brief Funct Genomics; 2023 May; 22(3):302-311. PubMed ID: 36715222 [TBL] [Abstract][Full Text] [Related]
3. iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Liu B; Li K; Huang DS; Chou KC Bioinformatics; 2018 Nov; 34(22):3835-3842. PubMed ID: 29878118 [TBL] [Abstract][Full Text] [Related]
4. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Liu B; Fang L; Long R; Lan X; Chou KC Bioinformatics; 2016 Feb; 32(3):362-9. PubMed ID: 26476782 [TBL] [Abstract][Full Text] [Related]
5. iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition. Lyu Y; Zhang Z; Li J; He W; Ding Y; Guo F IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2809-2815. PubMed ID: 33481715 [TBL] [Abstract][Full Text] [Related]
6. iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks. Yang H; Wang S; Xia X Anal Biochem; 2021 Oct; 630():114318. PubMed ID: 34364858 [TBL] [Abstract][Full Text] [Related]
7. iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree. Liang Y; Zhang S; Qiao H; Cheng Y Math Biosci Eng; 2021 Oct; 18(6):8797-8814. PubMed ID: 34814323 [TBL] [Abstract][Full Text] [Related]
8. iEnhancer-DLRA: identification of enhancers and their strengths by a self-attention fusion strategy for local and global features. Zeng L; Liu Y; Yu ZG; Liu Y Brief Funct Genomics; 2022 Sep; 21(5):399-407. PubMed ID: 35942693 [TBL] [Abstract][Full Text] [Related]
9. iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models. Li J; Wu Z; Lin W; Luo J; Zhang J; Chen Q; Chen J Bioinform Adv; 2023; 3(1):vbad043. PubMed ID: 37113248 [TBL] [Abstract][Full Text] [Related]
10. iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks. Nguyen QH; Nguyen-Vo TH; Le NQK; Do TTT; Rahardja S; Nguyen BP BMC Genomics; 2019 Dec; 20(Suppl 9):951. PubMed ID: 31874637 [TBL] [Abstract][Full Text] [Related]
11. iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding. Le NQK; Yapp EKY; Ho QT; Nagasundaram N; Ou YY; Yeh HY Anal Biochem; 2019 Apr; 571():53-61. PubMed ID: 30822398 [TBL] [Abstract][Full Text] [Related]
12. iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network. Xiao Z; Wang L; Ding Y; Yu L Methods; 2022 Dec; 208():1-8. PubMed ID: 36220606 [TBL] [Abstract][Full Text] [Related]
13. iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module. Jia J; Lei R; Qin L; Wu G; Wei X Front Genet; 2023; 14():1132018. PubMed ID: 36936423 [TBL] [Abstract][Full Text] [Related]
14. A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction. Amilpur S; Bhukya R J Bioinform Comput Biol; 2022 Apr; 20(2):2250005. PubMed ID: 35264081 [TBL] [Abstract][Full Text] [Related]
15. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Yu B; Qiu W; Chen C; Ma A; Jiang J; Zhou H; Ma Q Bioinformatics; 2020 Feb; 36(4):1074-1081. PubMed ID: 31603468 [TBL] [Abstract][Full Text] [Related]
16. iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength. Yang R; Wu F; Zhang C; Zhang L Int J Mol Sci; 2021 Mar; 22(7):. PubMed ID: 33808317 [TBL] [Abstract][Full Text] [Related]
17. SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength. Li Y; Kong F; Cui H; Wang F; Li C; Ma J IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):637-645. PubMed ID: 35015646 [TBL] [Abstract][Full Text] [Related]
18. iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention. Wang W; Wu Q; Li C BMC Genomics; 2023 Jul; 24(1):393. PubMed ID: 37442977 [TBL] [Abstract][Full Text] [Related]
19. BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone. Yang B; Liu F; Ren C; Ouyang Z; Xie Z; Bo X; Shu W Bioinformatics; 2017 Jul; 33(13):1930-1936. PubMed ID: 28334114 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]