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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

299 related articles for article (PubMed ID: 33450251)

  • 1. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations.
    Strokach A; Lu TY; Kim PM
    J Mol Biol; 2021 May; 433(11):166810. PubMed ID: 33450251
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity.
    Witvliet DK; Strokach A; Giraldo-Forero AF; Teyra J; Colak R; Kim PM
    Bioinformatics; 2016 May; 32(10):1589-91. PubMed ID: 26801957
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions.
    Strokach A; Corbi-Verge C; Teyra J; Kim PM
    Methods Mol Biol; 2019; 1851():1-17. PubMed ID: 30298389
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.
    Wang S; Sun S; Li Z; Zhang R; Xu J
    PLoS Comput Biol; 2017 Jan; 13(1):e1005324. PubMed ID: 28056090
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Flattening the curve-How to get better results with small deep-mutational-scanning datasets.
    Wirnsberger G; PritiĊĦanac I; Oberdorfer G; Gruber K
    Proteins; 2024 Jul; 92(7):886-902. PubMed ID: 38501649
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.
    Berliner N; Teyra J; Colak R; Garcia Lopez S; Kim PM
    PLoS One; 2014; 9(9):e107353. PubMed ID: 25243403
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting functional sites with an automated algorithm suitable for heterogeneous datasets.
    La D; Livesay DR
    BMC Bioinformatics; 2005 May; 6():116. PubMed ID: 15890082
    [TBL] [Abstract][Full Text] [Related]  

  • 8. ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.
    Li G; Yao S; Fan L
    J Chem Inf Model; 2024 Jan; 64(2):340-347. PubMed ID: 38166383
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improved protein contact predictions with the MetaPSICOV2 server in CASP12.
    Buchan DWA; Jones DT
    Proteins; 2018 Mar; 86 Suppl 1(Suppl Suppl 1):78-83. PubMed ID: 28901583
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.
    Hou J; Wu T; Cao R; Cheng J
    Proteins; 2019 Dec; 87(12):1165-1178. PubMed ID: 30985027
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challenge.
    Strokach A; Corbi-Verge C; Kim PM
    Hum Mutat; 2019 Sep; 40(9):1414-1423. PubMed ID: 31243847
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improving protein fold recognition using triplet network and ensemble deep learning.
    Liu Y; Han K; Zhu YH; Zhang Y; Shen LC; Song J; Yu DJ
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34226918
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dual graph convolutional neural network for predicting chemical networks.
    Harada S; Akita H; Tsubaki M; Baba Y; Takigawa I; Yamanishi Y; Kashima H
    BMC Bioinformatics; 2020 Apr; 21(Suppl 3):94. PubMed ID: 32321421
    [TBL] [Abstract][Full Text] [Related]  

  • 14. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.
    Cao R; Freitas C; Chan L; Sun M; Jiang H; Chen Z
    Molecules; 2017 Oct; 22(10):. PubMed ID: 29039790
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep-learning contact-map guided protein structure prediction in CASP13.
    Zheng W; Li Y; Zhang C; Pearce R; Mortuza SM; Zhang Y
    Proteins; 2019 Dec; 87(12):1149-1164. PubMed ID: 31365149
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. An analysis of protein language model embeddings for fold prediction.
    Villegas-Morcillo A; Gomez AM; Sanchez V
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35443054
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Pcons5: combining consensus, structural evaluation and fold recognition scores.
    Wallner B; Elofsson A
    Bioinformatics; 2005 Dec; 21(23):4248-54. PubMed ID: 16204344
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein.
    Raghava GP; Han JH
    BMC Bioinformatics; 2005 Mar; 6():59. PubMed ID: 15773999
    [TBL] [Abstract][Full Text] [Related]  

  • 20. EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.
    Jin Y; Lu J; Shi R; Yang Y
    Biomolecules; 2021 Nov; 11(12):. PubMed ID: 34944427
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.