BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

207 related articles for article (PubMed ID: 37789284)

  • 1. SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures.
    Lategan FA; Schreiber C; Patterton HG
    BMC Bioinformatics; 2023 Oct; 24(1):373. PubMed ID: 37789284
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks.
    Helles G; Fonseca R
    BMC Bioinformatics; 2009 Oct; 10():338. PubMed ID: 19835576
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.
    Li Z; Yang Y; Faraggi E; Zhan J; Zhou Y
    Proteins; 2014 Oct; 82(10):2565-73. PubMed ID: 24898915
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improved 3-D Protein Structure Predictions using Deep ResNet Model.
    Geethu S; Vimina ER
    Protein J; 2021 Oct; 40(5):669-681. PubMed ID: 34510309
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Highly accurate protein structure prediction with AlphaFold.
    Jumper J; Evans R; Pritzel A; Green T; Figurnov M; Ronneberger O; Tunyasuvunakool K; Bates R; Žídek A; Potapenko A; Bridgland A; Meyer C; Kohl SAA; Ballard AJ; Cowie A; Romera-Paredes B; Nikolov S; Jain R; Adler J; Back T; Petersen S; Reiman D; Clancy E; Zielinski M; Steinegger M; Pacholska M; Berghammer T; Bodenstein S; Silver D; Vinyals O; Senior AW; Kavukcuoglu K; Kohli P; Hassabis D
    Nature; 2021 Aug; 596(7873):583-589. PubMed ID: 34265844
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Applying and improving AlphaFold at CASP14.
    Jumper J; Evans R; Pritzel A; Green T; Figurnov M; Ronneberger O; Tunyasuvunakool K; Bates R; Žídek A; Potapenko A; Bridgland A; Meyer C; Kohl SAA; Ballard AJ; Cowie A; Romera-Paredes B; Nikolov S; Jain R; Adler J; Back T; Petersen S; Reiman D; Clancy E; Zielinski M; Steinegger M; Pacholska M; Berghammer T; Silver D; Vinyals O; Senior AW; Kavukcuoglu K; Kohli P; Hassabis D
    Proteins; 2021 Dec; 89(12):1711-1721. PubMed ID: 34599769
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.
    Stern J; Hedelius B; Fisher O; Billings WM; Della Corte D
    Int J Mol Sci; 2021 Nov; 22(23):. PubMed ID: 34884640
    [TBL] [Abstract][Full Text] [Related]  

  • 8. SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
    O'Connell J; Li Z; Hanson J; Heffernan R; Lyons J; Paliwal K; Dehzangi A; Yang Y; Zhou Y
    Proteins; 2018 Jun; 86(6):629-633. PubMed ID: 29508448
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
    Li AJ; Lu M; Desta I; Sundar V; Grigoryan G; Keating AE
    Protein Sci; 2023 Feb; 32(2):e4554. PubMed ID: 36564857
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Understanding the role of the topology in protein folding by computational inverse folding experiments.
    Mucherino A; Costantini S; di Serafino D; D'Apuzzo M; Facchiano A; Colonna G
    Comput Biol Chem; 2008 Aug; 32(4):233-9. PubMed ID: 18479970
    [TBL] [Abstract][Full Text] [Related]  

  • 12. De novo protein design by inversion of the AlphaFold structure prediction network.
    Goverde CA; Wolf B; Khakzad H; Rosset S; Correia BE
    Protein Sci; 2023 Jun; 32(6):e4653. PubMed ID: 37165539
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.
    Lyons J; Dehzangi A; Heffernan R; Sharma A; Paliwal K; Sattar A; Zhou Y; Yang Y
    J Comput Chem; 2014 Oct; 35(28):2040-6. PubMed ID: 25212657
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The whole is greater than its parts: ensembling improves protein contact prediction.
    Billings WM; Morris CJ; Della Corte D
    Sci Rep; 2021 Apr; 11(1):8039. PubMed ID: 33850214
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.
    Wang J; Watson JL; Lisanza SL
    Cold Spring Harb Perspect Biol; 2024 Jul; 16(7):. PubMed ID: 38438190
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computational Protein Design with Deep Learning Neural Networks.
    Wang J; Cao H; Zhang JZH; Qi Y
    Sci Rep; 2018 Apr; 8(1):6349. PubMed ID: 29679026
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improved protein structure prediction using potentials from deep learning.
    Senior AW; Evans R; Jumper J; Kirkpatrick J; Sifre L; Green T; Qin C; Žídek A; Nelson AWR; Bridgland A; Penedones H; Petersen S; Simonyan K; Crossan S; Kohli P; Jones DT; Silver D; Kavukcuoglu K; Hassabis D
    Nature; 2020 Jan; 577(7792):706-710. PubMed ID: 31942072
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet.
    Qi Y; Zhang JZH
    J Chem Inf Model; 2020 Mar; 60(3):1245-1252. PubMed ID: 32126171
    [TBL] [Abstract][Full Text] [Related]  

  • 19. De novo and inverse folding predictions of protein structure and dynamics.
    Godzik A; Kolinski A; Skolnick J
    J Comput Aided Mol Des; 1993 Aug; 7(4):397-438. PubMed ID: 8229093
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving computational protein design by using structure-derived sequence profile.
    Dai L; Yang Y; Kim HR; Zhou Y
    Proteins; 2010 Aug; 78(10):2338-48. PubMed ID: 20544969
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.