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Items: 1 to 20 of 105


Analysis of protein sequence and interaction data for candidate disease gene prediction.

George RA, Liu JY, Feng LL, Bryson-Richardson RJ, Fatkin D, Wouters MA.

Nucleic Acids Res. 2006;34(19):e130. Epub 2006 Oct 4.


Speeding disease gene discovery by sequence based candidate prioritization.

Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS.

BMC Bioinformatics. 2005 Mar 14;6:55.


A protein interaction based model for schizophrenia study.

Hsu PC, Yang UC, Shih KH, Liu CM, Liu YL, Hwu HG.

BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S23. doi: 10.1186/1471-2105-9-S12-S23.


A correlated motif approach for finding short linear motifs from protein interaction networks.

Tan SH, Hugo W, Sung WK, Ng SK.

BMC Bioinformatics. 2006 Nov 16;7:502.


Detecting disease genes based on semi-supervised learning and protein-protein interaction networks.

Nguyen TP, Ho TB.

Artif Intell Med. 2012 Jan;54(1):63-71. doi: 10.1016/j.artmed.2011.09.003. Epub 2011 Oct 14.


Gene prioritization in Type 2 Diabetes using domain interactions and network analysis.

Sharma A, Chavali S, Tabassum R, Tandon N, Bharadwaj D.

BMC Genomics. 2010 Feb 2;11:84. doi: 10.1186/1471-2164-11-84.


Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Tiffin N, Kelso JF, Powell AR, Pan H, Bajic VB, Hide WA.

Nucleic Acids Res. 2005 Mar 14;33(5):1544-52. Print 2005.


Network analysis of differential expression for the identification of disease-causing genes.

Nitsch D, Tranchevent LC, Thienpont B, Thorrez L, Van Esch H, Devriendt K, Moreau Y.

PLoS One. 2009;4(5):e5526. doi: 10.1371/journal.pone.0005526. Epub 2009 May 13.


Disease candidate gene identification and prioritization using protein interaction networks.

Chen J, Aronow BJ, Jegga AG.

BMC Bioinformatics. 2009 Feb 27;10:73. doi: 10.1186/1471-2105-10-73. Erratum in: BMC Bioinformatics. 2009;10:406.


Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach.

Zhang W, Sun F, Jiang R.

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S11. doi: 10.1186/1471-2105-12-S1-S11.


Predicting disease genes using protein-protein interactions.

Oti M, Snel B, Huynen MA, Brunner HG.

J Med Genet. 2006 Aug;43(8):691-8. Epub 2006 Apr 12.


Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression.

Zhang SW, Shao DD, Zhang SY, Wang YB.

Mol Biosyst. 2014 Jun;10(6):1400-8. doi: 10.1039/c3mb70588a. Epub 2014 Apr 3.


Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization.

Guney E, Oliva B.

PLoS One. 2012;7(9):e43557. doi: 10.1371/journal.pone.0043557. Epub 2012 Sep 21.


PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

Hawkins T, Chitale M, Luban S, Kihara D.

Proteins. 2009 Feb 15;74(3):566-82. doi: 10.1002/prot.22172.


A novel candidate disease genes prioritization method based on module partition and rank fusion.

Chen X, Yan GY, Liao XP.

OMICS. 2010 Aug;14(4):337-56. doi: 10.1089/omi.2009.0143.


Constructing a gene semantic similarity network for the inference of disease genes.

Jiang R, Gan M, He P.

BMC Syst Biol. 2011;5 Suppl 2:S2. doi: 10.1186/1752-0509-5-S2-S2. Epub 2011 Dec 14.


Prediction of human disease genes by human-mouse conserved coexpression analysis.

Ala U, Piro RM, Grassi E, Damasco C, Silengo L, Oti M, Provero P, Di Cunto F.

PLoS Comput Biol. 2008 Mar 28;4(3):e1000043. doi: 10.1371/journal.pcbi.1000043.


Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.

Keerthikumar S, Bhadra S, Kandasamy K, Raju R, Ramachandra YL, Bhattacharyya C, Imai K, Ohara O, Mohan S, Pandey A.

DNA Res. 2009 Dec;16(6):345-51. doi: 10.1093/dnares/dsp019. Epub 2009 Oct 3.


Discovering cancer genes by integrating network and functional properties.

Li L, Zhang K, Lee J, Cordes S, Davis DP, Tang Z.

BMC Med Genomics. 2009 Sep 19;2:61. doi: 10.1186/1755-8794-2-61.

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