313 related articles for article (PubMed ID: 23620363)
1. Predicting the functional consequences of cancer-associated amino acid substitutions.
Shihab HA; Gough J; Cooper DN; Day IN; Gaunt TR
Bioinformatics; 2013 Jun; 29(12):1504-10. PubMed ID: 23620363
[TBL] [Abstract][Full Text] [Related]
2. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.
Shihab HA; Gough J; Cooper DN; Stenson PD; Barker GL; Edwards KJ; Day IN; Gaunt TR
Hum Mutat; 2013 Jan; 34(1):57-65. PubMed ID: 23033316
[TBL] [Abstract][Full Text] [Related]
3. Predicting the functional consequences of somatic missense mutations found in tumors.
Carter H; Karchin R
Methods Mol Biol; 2014; 1101():135-59. PubMed ID: 24233781
[TBL] [Abstract][Full Text] [Related]
4. Ranking non-synonymous single nucleotide polymorphisms based on disease concepts.
Shihab HA; Gough J; Mort M; Cooper DN; Day IN; Gaunt TR
Hum Genomics; 2014 Jun; 8(1):11. PubMed ID: 24980617
[TBL] [Abstract][Full Text] [Related]
5. CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome.
Rogers MF; Gaunt TR; Campbell C
Bioinformatics; 2020 Jun; 36(12):3637-3644. PubMed ID: 32282885
[TBL] [Abstract][Full Text] [Related]
6. Assessment of computational methods for predicting the effects of missense mutations in human cancers.
Gnad F; Baucom A; Mukhyala K; Manning G; Zhang Z
BMC Genomics; 2013; 14 Suppl 3(Suppl 3):S7. PubMed ID: 23819521
[TBL] [Abstract][Full Text] [Related]
7. Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes.
Nono AD; Chen K; Liu X
BMC Med Genomics; 2019 Jan; 12(Suppl 1):22. PubMed ID: 30704472
[TBL] [Abstract][Full Text] [Related]
8. Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets.
Anoosha P; Sakthivel R; Michael Gromiha M
Biochim Biophys Acta; 2016 Feb; 1862(2):155-65. PubMed ID: 26581171
[TBL] [Abstract][Full Text] [Related]
9. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features.
Rogers MF; Shihab HA; Mort M; Cooper DN; Gaunt TR; Campbell C
Bioinformatics; 2018 Feb; 34(3):511-513. PubMed ID: 28968714
[TBL] [Abstract][Full Text] [Related]
10. Cancer Gene Discovery by Network Analysis of Somatic Mutations Using the MUFFINN Server.
Han H; Lehner B; Lee I
Methods Mol Biol; 2019; 1907():37-50. PubMed ID: 30542989
[TBL] [Abstract][Full Text] [Related]
11. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.
Merid SK; Goranskaya D; Alexeyenko A
BMC Bioinformatics; 2014 Sep; 15(1):308. PubMed ID: 25236784
[TBL] [Abstract][Full Text] [Related]
12. MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
Cho A; Shim JE; Kim E; Supek F; Lehner B; Lee I
Genome Biol; 2016 Jun; 17(1):129. PubMed ID: 27333808
[TBL] [Abstract][Full Text] [Related]
13. Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing.
Zhang J; Liu J; Sun J; Chen C; Foltz G; Lin B
Brief Bioinform; 2014 Mar; 15(2):244-55. PubMed ID: 23818492
[TBL] [Abstract][Full Text] [Related]
14. Snowball: resampling combined with distance-based regression to discover transcriptional consequences of a driver mutation.
Xu Y; Guo X; Sun J; Zhao Z
Bioinformatics; 2015 Jan; 31(1):84-93. PubMed ID: 25192743
[TBL] [Abstract][Full Text] [Related]
15. A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.
Tan H; Bao J; Zhou X
Bioinformatics; 2012 Nov; 28(22):2948-55. PubMed ID: 23044540
[TBL] [Abstract][Full Text] [Related]
16. Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.
Kumar S; Warrell J; Li S; McGillivray PD; Meyerson W; Salichos L; Harmanci A; Martinez-Fundichely A; Chan CWY; Nielsen MM; Lochovsky L; Zhang Y; Li X; Lou S; Pedersen JS; Herrmann C; Getz G; Khurana E; Gerstein MB
Cell; 2020 Mar; 180(5):915-927.e16. PubMed ID: 32084333
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.
Meyer MJ; Lapcevic R; Romero AE; Yoon M; Das J; Beltrán JF; Mort M; Stenson PD; Cooper DN; Paccanaro A; Yu H
Hum Mutat; 2016 May; 37(5):447-56. PubMed ID: 26841357
[TBL] [Abstract][Full Text] [Related]
19. Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes.
Banerjee S; Raman K; Ravindran B
Cancers (Basel); 2021 May; 13(10):. PubMed ID: 34068918
[TBL] [Abstract][Full Text] [Related]
20. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer.
Melloni GE; de Pretis S; Riva L; Pelizzola M; Céol A; Costanza J; Müller H; Zammataro L
BMC Bioinformatics; 2016 Feb; 17():80. PubMed ID: 26860319
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
