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Items: 1 to 20 of 148

1.

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants.

Capriotti E, Altman RB.

Genomics. 2011 Oct;98(4):310-7. doi: 10.1016/j.ygeno.2011.06.010. Epub 2011 Jul 7.

2.

Collective judgment predicts disease-associated single nucleotide variants.

Capriotti E, Altman RB, Bromberg Y.

BMC Genomics. 2013;14 Suppl 3:S2. doi: 10.1186/1471-2164-14-S3-S2. Epub 2013 May 28.

3.

Identifying novel oncogenes: a machine learning approach.

Kumar A, Rajendran V, Sethumadhavan R, Purohit R.

Interdiscip Sci. 2013 Dec;5(4):241-6. doi: 10.1007/s12539-013-0151-3. Epub 2014 Jan 10.

PMID:
24402816
4.

Improving the prediction of disease-related variants using protein three-dimensional structure.

Capriotti E, Altman RB.

BMC Bioinformatics. 2011;12 Suppl 4:S3. doi: 10.1186/1471-2105-12-S4-S3. Epub 2011 Jul 5.

5.

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:S7. doi: 10.1186/1471-2164-14-S3-S7. Epub 2013 May 28.

6.

Identifying Mendelian disease genes with the variant effect scoring tool.

Carter H, Douville C, Stenson PD, Cooper DN, Karchin R.

BMC Genomics. 2013;14 Suppl 3:S3. doi: 10.1186/1471-2164-14-S3-S3. Epub 2013 May 28.

7.

Predicting the functional consequences of somatic missense mutations found in tumors.

Carter H, Karchin R.

Methods Mol Biol. 2014;1101:135-59. doi: 10.1007/978-1-62703-721-1_8.

PMID:
24233781
8.

Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.

Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R.

Cancer Res. 2009 Aug 15;69(16):6660-7. doi: 10.1158/0008-5472.CAN-09-1133. Epub 2009 Aug 4.

9.

Bayesian approach to discovering pathogenic SNPs in conserved protein domains.

Cai Z, Tsung EF, Marinescu VD, Ramoni MF, Riva A, Kohane IS.

Hum Mutat. 2004 Aug;24(2):178-84.

PMID:
15241800
10.

Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.

Barenboim M, Masso M, Vaisman II, Jamison DC.

Proteins. 2008 Jun;71(4):1930-9. doi: 10.1002/prot.21838.

PMID:
18186470
11.

BRCA1 and BRCA2 unclassified variants and missense polymorphisms in Algerian breast/ovarian cancer families.

Cherbal F, Salhi N, Bakour R, Adane S, Boualga K, Maillet P.

Dis Markers. 2012;32(6):343-53. doi: 10.3233/DMA-2012-0893.

12.

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Listgarten J, Damaraju S, Poulin B, Cook L, Dufour J, Driga A, Mackey J, Wishart D, Greiner R, Zanke B.

Clin Cancer Res. 2004 Apr 15;10(8):2725-37.

13.

Application of machine learning in SNP discovery.

Matukumalli LK, Grefenstette JJ, Hyten DL, Choi IY, Cregan PB, Van Tassell CP.

BMC Bioinformatics. 2006 Jan 6;7:4.

14.

A method to predict the impact of regulatory variants from DNA sequence.

Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA.

Nat Genet. 2015 Aug;47(8):955-61. doi: 10.1038/ng.3331. Epub 2015 Jun 15.

15.

Performance of mutation pathogenicity prediction methods on missense variants.

Thusberg J, Olatubosun A, Vihinen M.

Hum Mutat. 2011 Apr;32(4):358-68. doi: 10.1002/humu.21445. Epub 2011 Feb 22.

PMID:
21412949
16.

Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.

Capriotti E, Arbiza L, Casadio R, Dopazo J, Dopazo H, Marti-Renom MA.

Hum Mutat. 2008 Jan;29(1):198-204.

PMID:
17935148
17.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms in human DNA repair genes.

Nakken S, Alseth I, Rognes T.

Neuroscience. 2007 Apr 14;145(4):1273-9. Epub 2006 Oct 19. Review.

PMID:
17055652
18.

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 28;20:25. doi: 10.1186/1423-0127-20-25.

19.

Improved feature-based prediction of SNPs in human cytochrome P450 enzymes.

Li L, Xiong Y, Zhang ZY, Guo Q, Xu Q, Liow HH, Zhang YH, Wei DQ.

Interdiscip Sci. 2015 Mar;7(1):65-77. doi: 10.1007/s12539-014-0257-2. Epub 2015 Mar 21.

PMID:
25792441
20.

Distinguishing cancer-associated missense mutations from common polymorphisms.

Kaminker JS, Zhang Y, Waugh A, Haverty PM, Peters B, Sebisanovic D, Stinson J, Forrest WF, Bazan JF, Seshagiri S, Zhang Z.

Cancer Res. 2007 Jan 15;67(2):465-73.

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