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.
228 related articles for article (PubMed ID: 30462171)
1. DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction. Elbasir A; Moovarkumudalvan B; Kunji K; Kolatkar PR; Mall R; Bensmail H Bioinformatics; 2019 Jul; 35(13):2216-2225. PubMed ID: 30462171 [TBL] [Abstract][Full Text] [Related]
2. BCrystal: an interpretable sequence-based protein crystallization predictor. Elbasir A; Mall R; Kunji K; Rawi R; Islam Z; Chuang GY; Kolatkar PR; Bensmail H Bioinformatics; 2020 Mar; 36(5):1429-1438. PubMed ID: 31603511 [TBL] [Abstract][Full Text] [Related]
3. CLPred: a sequence-based protein crystallization predictor using BLSTM neural network. Xuan W; Liu N; Huang N; Li Y; Wang J Bioinformatics; 2020 Dec; 36(Suppl_2):i709-i717. PubMed ID: 33381840 [TBL] [Abstract][Full Text] [Related]
4. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Khurana S; Rawi R; Kunji K; Chuang GY; Bensmail H; Mall R Bioinformatics; 2018 Aug; 34(15):2605-2613. PubMed ID: 29554211 [TBL] [Abstract][Full Text] [Related]
5. Crysalis: an integrated server for computational analysis and design of protein crystallization. Wang H; Feng L; Zhang Z; Webb GI; Lin D; Song J Sci Rep; 2016 Feb; 6():21383. PubMed ID: 26906024 [TBL] [Abstract][Full Text] [Related]
6. 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]
7. Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features. Zhu YH; Hu J; Ge F; Li F; Song J; Zhang Y; Yu DJ Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32436937 [TBL] [Abstract][Full Text] [Related]
8. Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. Wang H; Feng L; Webb GI; Kurgan L; Song J; Lin D Brief Bioinform; 2018 Sep; 19(5):838-852. PubMed ID: 28334201 [TBL] [Abstract][Full Text] [Related]
9. SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks. Wang S; Zhao H Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36037090 [TBL] [Abstract][Full Text] [Related]
10. Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity. Zhu Q; Wang L; Dai R; Zhang W; Tang W; Bin Y; Wang Z; Xia J Interdiscip Sci; 2021 Dec; 13(4):693-702. PubMed ID: 34143353 [TBL] [Abstract][Full Text] [Related]
11. Protein-protein interaction site prediction through combining local and global features with deep neural networks. Zeng M; Zhang F; Wu FX; Li Y; Wang J; Li M Bioinformatics; 2020 Feb; 36(4):1114-1120. PubMed ID: 31593229 [TBL] [Abstract][Full Text] [Related]
12. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks. Adhikari B; Hou J; Cheng J Bioinformatics; 2018 May; 34(9):1466-1472. PubMed ID: 29228185 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. DELPHI: accurate deep ensemble model for protein interaction sites prediction. Li Y; Golding GB; Ilie L Bioinformatics; 2021 May; 37(7):896-904. PubMed ID: 32840562 [TBL] [Abstract][Full Text] [Related]
15. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. Zhang Y; Yu S; Xie R; Li J; Leier A; Marquez-Lago TT; Akutsu T; Smith AI; Ge Z; Wang J; Lithgow T; Song J Bioinformatics; 2020 Feb; 36(3):704-712. PubMed ID: 31393553 [TBL] [Abstract][Full Text] [Related]
16. DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment. Fukuda H; Tomii K BMC Bioinformatics; 2020 Jan; 21(1):10. PubMed ID: 31918654 [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. PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning. Li P; Liu ZP Bioinformatics; 2022 Apr; 38(8):2162-2168. PubMed ID: 35150250 [TBL] [Abstract][Full Text] [Related]