133 related articles for article (PubMed ID: 34718416)
1. CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.
Feng SH; Xia CQ; Shen HB
Bioinformatics; 2022 Jan; 38(3):720-729. PubMed ID: 34718416
[TBL] [Abstract][Full Text] [Related]
2. CoCoNat: a novel method based on deep learning for coiled-coil prediction.
Madeo G; Savojardo C; Manfredi M; Martelli PL; Casadio R
Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37540220
[TBL] [Abstract][Full Text] [Related]
3. Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins.
Li C; Ching Han Chang C; Nagel J; Porebski BT; Hayashida M; Akutsu T; Song J; Buckle AM
Brief Bioinform; 2016 Mar; 17(2):270-82. PubMed ID: 26177815
[TBL] [Abstract][Full Text] [Related]
4. DeepCoil-a fast and accurate prediction of coiled-coil domains in protein sequences.
Ludwiczak J; Winski A; Szczepaniak K; Alva V; Dunin-Horkawicz S
Bioinformatics; 2019 Aug; 35(16):2790-2795. PubMed ID: 30601942
[TBL] [Abstract][Full Text] [Related]
5. DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction.
Guo Y; Li W; Wang B; Liu H; Zhou D
BMC Bioinformatics; 2019 Jun; 20(1):341. PubMed ID: 31208331
[TBL] [Abstract][Full Text] [Related]
6. Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices.
Li C; Wang XF; Chen Z; Zhang Z; Song J
Mol Biosyst; 2015 Feb; 11(2):354-60. PubMed ID: 25435395
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. LOGICOIL--multi-state prediction of coiled-coil oligomeric state.
Vincent TL; Green PJ; Woolfson DN
Bioinformatics; 2013 Jan; 29(1):69-76. PubMed ID: 23129295
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Performing protein fold recognition by exploiting a stack convolutional neural network with the attention mechanism.
Han K; Liu Y; Xu J; Song J; Yu DJ
Anal Biochem; 2022 Aug; 651():114695. PubMed ID: 35487269
[TBL] [Abstract][Full Text] [Related]
11. Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.
Heffernan R; Yang Y; Paliwal K; Zhou Y
Bioinformatics; 2017 Sep; 33(18):2842-2849. PubMed ID: 28430949
[TBL] [Abstract][Full Text] [Related]
12. RNA-binding protein recognition based on multi-view deep feature and multi-label learning.
Yang H; Deng Z; Pan X; Shen HB; Choi KS; Wang L; Wang S; Wu J
Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32808039
[TBL] [Abstract][Full Text] [Related]
13. Designability landscape reveals sequence features that define axial helix rotation in four-helical homo-oligomeric antiparallel coiled-coil structures.
Szczepaniak K; Lach G; Bujnicki JM; Dunin-Horkawicz S
J Struct Biol; 2014 Nov; 188(2):123-33. PubMed ID: 25278129
[TBL] [Abstract][Full Text] [Related]
14. The evolution and structure prediction of coiled coils across all genomes.
Rackham OJ; Madera M; Armstrong CT; Vincent TL; Woolfson DN; Gough J
J Mol Biol; 2010 Oct; 403(3):480-93. PubMed ID: 20813113
[TBL] [Abstract][Full Text] [Related]
15. Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring.
Zhang WX; Pan X; Shen HB
J Chem Inf Model; 2020 Jul; 60(7):3679-3686. PubMed ID: 32501689
[TBL] [Abstract][Full Text] [Related]
16. A library of coiled-coil domains: from regular bundles to peculiar twists.
Szczepaniak K; Bukala A; da Silva Neto AM; Ludwiczak J; Dunin-Horkawicz S
Bioinformatics; 2021 Apr; 36(22-23):5368-5376. PubMed ID: 33325494
[TBL] [Abstract][Full Text] [Related]
17. High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.
Jones DT; Kandathil SM
Bioinformatics; 2018 Oct; 34(19):3308-3315. PubMed ID: 29718112
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.
Liu Y; Zhu YH; Song X; Song J; Yu DJ
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33537753
[TBL] [Abstract][Full Text] [Related]
20. Bioinformatics Analysis of the Periodicity in Proteins with Coiled-Coil Structure-Enumerating All Decompositions of Sequence Periods.
Then A; Zhang H; Ibrahim B; Schuster S
Int J Mol Sci; 2022 Aug; 23(15):. PubMed ID: 35955828
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]