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.
2. Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides. Martín-Navarro A; Gaudioso-Simón A; Álvarez-Jarreta J; Montoya J; Mayordomo E; Ruiz-Pesini E BMC Bioinformatics; 2017 Mar; 18(1):158. PubMed ID: 28270093 [TBL] [Abstract][Full Text] [Related]
3. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Høie MH; Cagiada M; Beck Frederiksen AH; Stein A; Lindorff-Larsen K Cell Rep; 2022 Jan; 38(2):110207. PubMed ID: 35021073 [TBL] [Abstract][Full Text] [Related]
4. Cross-protein transfer learning substantially improves disease variant prediction. Jagota M; Ye C; Albors C; Rastogi R; Koehl A; Ioannidis N; Song YS Genome Biol; 2023 Aug; 24(1):182. PubMed ID: 37550700 [TBL] [Abstract][Full Text] [Related]
5. Predicting the oncogenicity of missense mutations reported in the International Agency for Cancer Research (IARC) mutation database on p53. Gorlov IP; Gorlova OY; Amos CI Hum Mutat; 2005 Nov; 26(5):446-54. PubMed ID: 16173033 [TBL] [Abstract][Full Text] [Related]
6. Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization. Arani AA; Sehhati M; Tabatabaiefar MA Sci Rep; 2021 Dec; 11(1):23747. PubMed ID: 34887492 [TBL] [Abstract][Full Text] [Related]
7. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Mathe E; Olivier M; Kato S; Ishioka C; Hainaut P; Tavtigian SV Nucleic Acids Res; 2006; 34(5):1317-25. PubMed ID: 16522644 [TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. MVP predicts the pathogenicity of missense variants by deep learning. Qi H; Zhang H; Zhao Y; Chen C; Long JJ; Chung WK; Guan Y; Shen Y Nat Commun; 2021 Jan; 12(1):510. PubMed ID: 33479230 [TBL] [Abstract][Full Text] [Related]
11. Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges. Pejaver V; Mooney SD; Radivojac P Hum Mutat; 2017 Sep; 38(9):1092-1108. PubMed ID: 28508593 [TBL] [Abstract][Full Text] [Related]
12. SIGMA leverages protein structural information to predict the pathogenicity of missense variants. Zhao H; Du H; Zhao S; Chen Z; Li Y; Xu K; Liu B; Cheng X; Wen W; Li G; Chen G; Zhao Z; Qiu G; ; Liu P; Zhang TJ; Wu Z; Wu N Cell Rep Methods; 2024 Jan; 4(1):100687. PubMed ID: 38211594 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms. Masso M; Vaisman II J Theor Biol; 2010 Oct; 266(4):560-8. PubMed ID: 20655929 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. Structure-based prediction of the effects of a missense variant on protein stability. Yang Y; Chen B; Tan G; Vihinen M; Shen B Amino Acids; 2013 Mar; 44(3):847-55. PubMed ID: 23064876 [TBL] [Abstract][Full Text] [Related]
17. PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting. Dereli O; Kuru N; Akkoyun E; Bircan A; Tastan O; Adebali O Mol Biol Evol; 2024 Jul; 41(7):. PubMed ID: 38934805 [TBL] [Abstract][Full Text] [Related]
18. MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants. Gosalia N; Economides AN; Dewey FE; Balasubramanian S Nucleic Acids Res; 2017 Oct; 45(18):10393-10402. PubMed ID: 28977528 [TBL] [Abstract][Full Text] [Related]
19. mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants. Tong SY; Fan K; Zhou ZW; Liu LY; Zhang SQ; Fu Y; Wang GZ; Zhu Y; Yu YC Genomics Proteomics Bioinformatics; 2023 Apr; 21(2):414-426. PubMed ID: 35940520 [TBL] [Abstract][Full Text] [Related]
20. CRIMEtoYHU: a new web tool to develop yeast-based functional assays for characterizing cancer-associated missense variants. Mercatanti A; Lodovichi S; Cervelli T; Galli A FEMS Yeast Res; 2017 Dec; 17(8):. PubMed ID: 29069390 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]