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 *

202 related articles for article (PubMed ID: 37243972)

  • 1. Correspondence between functional scores from deep mutational scans and predicted effects on protein stability.
    Gerasimavicius L; Livesey BJ; Marsh JA
    Protein Sci; 2023 Jul; 32(7):e4688. PubMed ID: 37243972
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.
    Fu Y; Bedő J; Papenfuss AT; Rubin AF
    Gigascience; 2022 Dec; 12():. PubMed ID: 37721410
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations.
    Livesey BJ; Marsh JA
    Mol Syst Biol; 2020 Jul; 16(7):e9380. PubMed ID: 32627955
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Interpreting protein variant effects with computational predictors and deep mutational scanning.
    Livesey BJ; Marsh JA
    Dis Model Mech; 2022 Jun; 15(6):. PubMed ID: 35736673
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification of pathogenic missense mutations using protein stability predictors.
    Gerasimavicius L; Liu X; Marsh JA
    Sci Rep; 2020 Sep; 10(1):15387. PubMed ID: 32958805
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Variant effect predictions capture some aspects of deep mutational scanning experiments.
    Reeb J; Wirth T; Rost B
    BMC Bioinformatics; 2020 Mar; 21(1):107. PubMed ID: 32183714
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Updated benchmarking of variant effect predictors using deep mutational scanning.
    Livesey BJ; Marsh JA
    Mol Syst Biol; 2023 Aug; 19(8):e11474. PubMed ID: 37310135
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting mutant outcome by combining deep mutational scanning and machine learning.
    Sarfati H; Naftaly S; Papo N; Keasar C
    Proteins; 2022 Jan; 90(1):45-57. PubMed ID: 34293212
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.
    Nisthal A; Wang CY; Ary ML; Mayo SL
    Proc Natl Acad Sci U S A; 2019 Aug; 116(33):16367-16377. PubMed ID: 31371509
    [TBL] [Abstract][Full Text] [Related]  

  • 10. EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning.
    Nagar N; Tubiana J; Loewenthal G; Wolfson HJ; Ben Tal N; Pupko T
    J Mol Biol; 2023 Jul; 435(14):168155. PubMed ID: 37356902
    [TBL] [Abstract][Full Text] [Related]  

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

  • 12. Embeddings from protein language models predict conservation and variant effects.
    Marquet C; Heinzinger M; Olenyi T; Dallago C; Erckert K; Bernhofer M; Nechaev D; Rost B
    Hum Genet; 2022 Oct; 141(10):1629-1647. PubMed ID: 34967936
    [TBL] [Abstract][Full Text] [Related]  

  • 13. DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction.
    Munro D; Singh M
    Bioinformatics; 2021 Apr; 36(22-23):5322-5329. PubMed ID: 33325500
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Software for the analysis and visualization of deep mutational scanning data.
    Bloom JD
    BMC Bioinformatics; 2015 May; 16():168. PubMed ID: 25990960
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies.
    Faure AJ; Schmiedel JM; Baeza-Centurion P; Lehner B
    Genome Biol; 2020 Aug; 21(1):207. PubMed ID: 32799905
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Performance of protein stability predictors.
    Khan S; Vihinen M
    Hum Mutat; 2010 Jun; 31(6):675-84. PubMed ID: 20232415
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.
    Pandurangan AP; Blundell TL
    Protein Sci; 2020 Jan; 29(1):247-257. PubMed ID: 31693276
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PremPS: Predicting the impact of missense mutations on protein stability.
    Chen Y; Lu H; Zhang N; Zhu Z; Wang S; Li M
    PLoS Comput Biol; 2020 Dec; 16(12):e1008543. PubMed ID: 33378330
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Best templates outperform homology models in predicting the impact of mutations on protein stability.
    Pak MA; Ivankov DN
    Bioinformatics; 2022 Sep; 38(18):4312-4320. PubMed ID: 35894930
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations.
    Iqbal S; Li F; Akutsu T; Ascher DB; Webb GI; Song J
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34058752
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.