154 related articles for article (PubMed ID: 37092035)
1. SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.
Hasan AKMM; Ahmed AY; Mahbub S; Rahman MS; Bayzid MS
Bioinform Adv; 2023; 3(1):vbad042. PubMed ID: 37092035
[TBL] [Abstract][Full Text] [Related]
2. SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction.
Uddin MR; Mahbub S; Rahman MS; Bayzid MS
Bioinformatics; 2020 Nov; 36(17):4599-4608. PubMed ID: 32437517
[TBL] [Abstract][Full Text] [Related]
3. OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks.
Xu G; Wang Q; Ma J
Bioinformatics; 2020 Dec; 36(20):5021-5026. PubMed ID: 32678893
[TBL] [Abstract][Full Text] [Related]
4. Deep learning methods for protein torsion angle prediction.
Li H; Hou J; Adhikari B; Lyu Q; Cheng J
BMC Bioinformatics; 2017 Sep; 18(1):417. PubMed ID: 28923002
[TBL] [Abstract][Full Text] [Related]
5. IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility.
Gormez Y; Aydin Z
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1104-1113. PubMed ID: 35849663
[TBL] [Abstract][Full Text] [Related]
6. TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences.
Song J; Tan H; Wang M; Webb GI; Akutsu T
PLoS One; 2012; 7(2):e30361. PubMed ID: 22319565
[TBL] [Abstract][Full Text] [Related]
7. Prediction of Protein Backbone Torsion Angles Using Deep Residual Inception Neural Networks.
Fang C; Shang Y; Xu D
IEEE/ACM Trans Comput Biol Bioinform; 2018 Mar; ():. PubMed ID: 29994074
[TBL] [Abstract][Full Text] [Related]
8. TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences.
Kabir MWU; Alawad DM; Mishra A; Hoque MT
Biology (Basel); 2023 Jul; 12(7):. PubMed ID: 37508449
[TBL] [Abstract][Full Text] [Related]
9. MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns.
Fang C; Li Z; Xu D; Shang Y
Bioinformatics; 2020 Feb; 36(4):1293-1295. PubMed ID: 31532508
[TBL] [Abstract][Full Text] [Related]
10. SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.
Singh J; Litfin T; Paliwal K; Singh J; Hanumanthappa AK; Zhou Y
Bioinformatics; 2021 Oct; 37(20):3464-3472. PubMed ID: 33983382
[TBL] [Abstract][Full Text] [Related]
11. Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction.
Zhang T; Faraggi E; Zhou Y
Proteins; 2010 Dec; 78(16):3353-62. PubMed ID: 20818661
[TBL] [Abstract][Full Text] [Related]
12. EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction.
Mahbub S; Bayzid MS
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35106547
[TBL] [Abstract][Full Text] [Related]
13. Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.
Broz M; Jukič M; Bren U
Molecules; 2023 Oct; 28(20):. PubMed ID: 37894526
[TBL] [Abstract][Full Text] [Related]
14. Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.
Hanson J; Paliwal K; Litfin T; Yang Y; Zhou Y
Bioinformatics; 2019 Jul; 35(14):2403-2410. PubMed ID: 30535134
[TBL] [Abstract][Full Text] [Related]
15. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.
Heffernan R; Paliwal K; Lyons J; Dehzangi A; Sharma A; Wang J; Sattar A; Yang Y; Zhou Y
Sci Rep; 2015 Jun; 5():11476. PubMed ID: 26098304
[TBL] [Abstract][Full Text] [Related]
16. Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures.
Gao J; Yang Y; Zhou Y
BMC Bioinformatics; 2018 Feb; 19(1):29. PubMed ID: 29390958
[TBL] [Abstract][Full Text] [Related]
17. Protein backbone angle prediction with machine learning approaches.
Kuang R; Leslie CS; Yang AS
Bioinformatics; 2004 Jul; 20(10):1612-21. PubMed ID: 14988121
[TBL] [Abstract][Full Text] [Related]
18. Accurate prediction of protein torsion angles using chemical shifts and sequence homology.
Neal S; Berjanskii M; Zhang H; Wishart DS
Magn Reson Chem; 2006 Jul; 44 Spec No():S158-67. PubMed ID: 16823900
[TBL] [Abstract][Full Text] [Related]
19. AMIGOS III: pseudo-torsion angle visualization and motif-based structure comparison of nucleic acids.
Shine M; Zhang C; Pyle AM
Bioinformatics; 2022 May; 38(10):2937-2939. PubMed ID: 35561202
[TBL] [Abstract][Full Text] [Related]
20. Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.
Xu YC; ShangGuan TJ; Ding XM; Cheung NJ
Sci Rep; 2021 Oct; 11(1):21033. PubMed ID: 34702851
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
