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.
504 related articles for article (PubMed ID: 30423078)
1. A deep neural network approach for learning intrinsic protein-RNA binding preferences. Ben-Bassat I; Chor B; Orenstein Y Bioinformatics; 2018 Sep; 34(17):i638-i646. PubMed ID: 30423078 [TBL] [Abstract][Full Text] [Related]
2. Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. Pan X; Shen HB Bioinformatics; 2018 Oct; 34(20):3427-3436. PubMed ID: 29722865 [TBL] [Abstract][Full Text] [Related]
3. Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction. Su Y; Luo Y; Zhao X; Liu Y; Peng J PLoS Comput Biol; 2019 Sep; 15(9):e1007283. PubMed ID: 31483777 [TBL] [Abstract][Full Text] [Related]
4. Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities. Trabelsi A; Chaabane M; Ben-Hur A Bioinformatics; 2019 Jul; 35(14):i269-i277. PubMed ID: 31510640 [TBL] [Abstract][Full Text] [Related]
5. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. Pan X; Shen HB BMC Bioinformatics; 2017 Feb; 18(1):136. PubMed ID: 28245811 [TBL] [Abstract][Full Text] [Related]
6. DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network. Shi Q; Chen W; Huang S; Jin F; Dong Y; Wang Y; Xue Z Bioinformatics; 2019 Dec; 35(24):5128-5136. PubMed ID: 31197306 [TBL] [Abstract][Full Text] [Related]
7. Predicting protein-ligand binding residues with deep convolutional neural networks. Cui Y; Dong Q; Hong D; Wang X BMC Bioinformatics; 2019 Feb; 20(1):93. PubMed ID: 30808287 [TBL] [Abstract][Full Text] [Related]
8. Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure. Deng L; Liu Y; Shi Y; Zhang W; Yang C; Liu H BMC Genomics; 2020 Dec; 21(Suppl 13):866. PubMed ID: 33334313 [TBL] [Abstract][Full Text] [Related]
9. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Tsubaki M; Tomii K; Sese J Bioinformatics; 2019 Jan; 35(2):309-318. PubMed ID: 29982330 [TBL] [Abstract][Full Text] [Related]
10. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. Pan X; Rijnbeek P; Yan J; Shen HB BMC Genomics; 2018 Jul; 19(1):511. PubMed ID: 29970003 [TBL] [Abstract][Full Text] [Related]
11. Prediction of mRNA subcellular localization using deep recurrent neural networks. Yan Z; Lécuyer E; Blanchette M Bioinformatics; 2019 Jul; 35(14):i333-i342. PubMed ID: 31510698 [TBL] [Abstract][Full Text] [Related]
12. DeepPhos: prediction of protein phosphorylation sites with deep learning. Luo F; Wang M; Liu Y; Zhao XM; Li A Bioinformatics; 2019 Aug; 35(16):2766-2773. PubMed ID: 30601936 [TBL] [Abstract][Full Text] [Related]
13. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. Hirohara M; Saito Y; Koda Y; Sato K; Sakakibara Y BMC Bioinformatics; 2018 Dec; 19(Suppl 19):526. PubMed ID: 30598075 [TBL] [Abstract][Full Text] [Related]
14. Deep learning of the back-splicing code for circular RNA formation. Wang J; Wang L Bioinformatics; 2019 Dec; 35(24):5235-5242. PubMed ID: 31077303 [TBL] [Abstract][Full Text] [Related]
15. GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text. Zhu Q; Li X; Conesa A; Pereira C Bioinformatics; 2018 May; 34(9):1547-1554. PubMed ID: 29272325 [TBL] [Abstract][Full Text] [Related]
16. ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins. Yang S; Liu X; Ng RT Bioinformatics; 2020 Sep; 36(18):4797-4804. PubMed ID: 32573679 [TBL] [Abstract][Full Text] [Related]
17. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Karimi M; Wu D; Wang Z; Shen Y Bioinformatics; 2019 Sep; 35(18):3329-3338. PubMed ID: 30768156 [TBL] [Abstract][Full Text] [Related]
18. Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks. Guo Y; Wang B; Li W; Yang B J Bioinform Comput Biol; 2018 Oct; 16(5):1850021. PubMed ID: 30419785 [TBL] [Abstract][Full Text] [Related]
19. miTAR: a hybrid deep learning-based approach for predicting miRNA targets. Gu T; Zhao X; Barbazuk WB; Lee JH BMC Bioinformatics; 2021 Feb; 22(1):96. PubMed ID: 33639834 [TBL] [Abstract][Full Text] [Related]
20. Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions. Yan Z; Hamilton WL; Blanchette M Bioinformatics; 2020 Jul; 36(Suppl_1):i276-i284. PubMed ID: 32657407 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]