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.
258 related articles for article (PubMed ID: 37084271)
1. Predicting the pathogenicity of missense variants using features derived from AlphaFold2. Schmidt A; Röner S; Mai K; Klinkhammer H; Kircher M; Ludwig KU Bioinformatics; 2023 May; 39(5):. PubMed ID: 37084271 [TBL] [Abstract][Full Text] [Related]
2. Accuracy of a machine learning method based on structural and locational information from AlphaFold2 for predicting the pathogenicity of TARDBP and FUS gene variants in ALS. Hatano Y; Ishihara T; Onodera O BMC Bioinformatics; 2023 May; 24(1):206. PubMed ID: 37208601 [TBL] [Abstract][Full Text] [Related]
3. AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes. Ahmad RM; Ali BR; Al-Jasmi F; Al Dhaheri N; Al Turki S; Kizhakkedath P; Mohamad MS Hum Genomics; 2024 Sep; 18(1):99. PubMed ID: 39256852 [TBL] [Abstract][Full Text] [Related]
4. Curated multiple sequence alignment for the Adenomatous Polyposis Coli (APC) gene and accuracy of in silico pathogenicity predictions. Karabachev AD; Martini DJ; Hermel DJ; Solcz D; Richardson ME; Pesaran T; Sarkar IN; Greenblatt MS PLoS One; 2020; 15(8):e0233673. PubMed ID: 32750050 [TBL] [Abstract][Full Text] [Related]
5. Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures. Li C; Zhang L; Zhuo Z; Su F; Li H; Xu S; Liu Y; Zhang Z; Xie Y; Yu X; Bian L; Xiao F Theranostics; 2023; 13(1):391-402. PubMed ID: 36593954 [TBL] [Abstract][Full Text] [Related]
6. 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]
7. Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations. Wang B; Lei X; Tian W; Perez-Rathke A; Tseng YY; Liang J Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37332013 [TBL] [Abstract][Full Text] [Related]
8. 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]
10. PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner. Zeng B; Liu DC; Huang JG; Xia XB; Qin B Hum Genet; 2024 Mar; 143(3):331-342. PubMed ID: 38478153 [TBL] [Abstract][Full Text] [Related]
11. Evaluating the use of paralogous protein domains to increase data availability for missense variant classification. Gunning AC; Wright CF Genome Med; 2023 Dec; 15(1):110. PubMed ID: 38087376 [TBL] [Abstract][Full Text] [Related]
12. Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants. Capriotti E; Fariselli P Hum Genet; 2022 Oct; 141(10):1649-1658. PubMed ID: 35098354 [TBL] [Abstract][Full Text] [Related]
13. MTR3D-AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2. Kovacs AS; Portelli S; Silk M; Rodrigues CHM; Ascher DB Protein Sci; 2024 Aug; 33(8):e5112. PubMed ID: 39031445 [TBL] [Abstract][Full Text] [Related]
14. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. Quinodoz M; Peter VG; Cisarova K; Royer-Bertrand B; Stenson PD; Cooper DN; Unger S; Superti-Furga A; Rivolta C Am J Hum Genet; 2022 Mar; 109(3):457-470. PubMed ID: 35120630 [TBL] [Abstract][Full Text] [Related]
15. KVarPredDB: a database for predicting pathogenicity of missense sequence variants of keratin genes associated with genodermatoses. Ying Y; Lu L; Banerjee S; Xu L; Zhao Q; Wu H; Li R; Xu X; Yu H; Neculai D; Xi Y; Yang F; Qin J; Li C Hum Genomics; 2020 Dec; 14(1):45. PubMed ID: 33287903 [TBL] [Abstract][Full Text] [Related]
16. Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome. Mohammed EEA; Fayez AG; Abdelfattah NM; Fateen E Sci Rep; 2024 May; 14(1):12148. PubMed ID: 38802532 [TBL] [Abstract][Full Text] [Related]
17. Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels. Cannon S; Williams M; Gunning AC; Wright CF BMC Med Genomics; 2023 Feb; 16(1):36. PubMed ID: 36855133 [TBL] [Abstract][Full Text] [Related]
18. In-silico Analysis of Accetturo M; Bartolomeo N; Stella A Int J Mol Sci; 2020 Jan; 21(3):. PubMed ID: 31979111 [No Abstract] [Full Text] [Related]
19. Impact of the Mutational Landscape of the Sodium/Iodide Symporter in Congenital Hypothyroidism. Martín M; Nicola JP Thyroid; 2021 Dec; 31(12):1776-1785. PubMed ID: 34514854 [No Abstract] [Full Text] [Related]
20. CoDP: predicting the impact of unclassified genetic variants in MSH6 by the combination of different properties of the protein. Terui H; Akagi K; Kawame H; Yura K J Biomed Sci; 2013 Apr; 20(1):25. PubMed ID: 23621914 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]