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Items: 1 to 20 of 87

1.

Integrative systems biology approaches to identify and prioritize disease and drug candidate genes.

Kaimal V, Sardana D, Bardes EE, Gudivada RC, Chen J, Jegga AG.

Methods Mol Biol. 2011;700:241-59. doi: 10.1007/978-1-61737-954-3_16.

PMID:
21204038
2.

Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge.

Gudivada RC, Qu XA, Chen J, Jegga AG, Neumann EK, Aronow BJ.

J Biomed Inform. 2008 Oct;41(5):717-29. doi: 10.1016/j.jbi.2008.07.004.

3.

CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data.

Ma X, Lee H, Wang L, Sun F.

Bioinformatics. 2007 Jan 15;23(2):215-21.

PMID:
17098772
4.

Gene prioritization through genomic data fusion.

Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, Tranchevent LC, De Moor B, Marynen P, Hassan B, Carmeliet P, Moreau Y.

Nat Biotechnol. 2006 May;24(5):537-44. Erratum in: Nat Biotechnol. 2006 Jun;24(6):719.

PMID:
16680138
5.

Web tools for the prioritization of candidate disease genes.

Oti M, Ballouz S, Wouters MA.

Methods Mol Biol. 2011;760:189-206. doi: 10.1007/978-1-61779-176-5_12.

PMID:
21779998
6.

Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks.

Zhou X, Liu B, Wu Z, Feng Y.

Artif Intell Med. 2007 Oct;41(2):87-104.

PMID:
17804209
7.

Predicting cancer involvement of genes from heterogeneous data.

Aragues R, Sander C, Oliva B.

BMC Bioinformatics. 2008 Mar 27;9:172. doi: 10.1186/1471-2105-9-172.

8.

In silico identification of new genetic variations as potential risk factors for Alzheimer's disease in a microarray-oriented simulation.

Lemos RR, Castelletti CH, Lima Filho JL, Marques ET, Oliveira JR.

J Mol Neurosci. 2009 Sep;39(1-2):242-7. doi: 10.1007/s12031-009-9191-x.

PMID:
19290494
9.

Prioritizing genes of potential relevance to diseases affected by sex hormones: an example of myasthenia gravis.

Kaur M, Schmeier S, MacPherson CR, Hofmann O, Hide WA, Taylor S, Willcox N, Bajic VB.

BMC Genomics. 2008 Oct 13;9:481. doi: 10.1186/1471-2164-9-481.

10.

A systems biology approach to drug discovery.

Zhu J, Zhang B, Schadt EE.

Adv Genet. 2008;60:603-35. doi: 10.1016/S0065-2660(07)00421-X. Review.

PMID:
18358334
11.

Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases.

Gefen A, Cohen R, Birk OS.

Hum Mutat. 2010 Mar;31(3):229-36. doi: 10.1002/humu.21171.

PMID:
20052752
12.

Gene Prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases.

Yu W, Wulf A, Liu T, Khoury MJ, Gwinn M.

BMC Bioinformatics. 2008 Dec 8;9:528. doi: 10.1186/1471-2105-9-528.

13.

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks.

Chen L, Tai J, Zhang L, Shang Y, Li X, Qu X, Li W, Miao Z, Jia X, Wang H, Li W, He W.

Mol Biosyst. 2011 Sep;7(9):2547-53. doi: 10.1039/c1mb05134b.

PMID:
21735017
14.
15.

Integrating genetic, functional genomic, and bioinformatics data in a systems biology approach to complex diseases: application to schizophrenia.

Middleton FA, Rosenow C, Vailaya A, Kuchinsky A, Pato MT, Pato CN.

Methods Mol Biol. 2007;401:337-64. doi: 10.1007/978-1-59745-520-6_18. Review.

PMID:
18368374
16.

Conceptual thinking for in silico prioritization of candidate disease genes.

Tiffin N.

Methods Mol Biol. 2011;760:175-87. doi: 10.1007/978-1-61779-176-5_11.

PMID:
21779997
17.

A GMM-IG framework for selecting genes as expression panel biomarkers.

Wang M, Chen JY.

Artif Intell Med. 2010 Feb-Mar;48(2-3):75-82. doi: 10.1016/j.artmed.2009.07.006.

PMID:
20004087
18.

New insight into the genes susceptible to asthma.

Gu ML, Dong XQ, Zhao J.

J Asthma. 2010 Mar;47(2):113-6. doi: 10.3109/02770900903498442.

PMID:
20170315
19.
20.

Gene- and evidence-based candidate gene selection for schizophrenia and gene feature analysis.

Sun J, Han L, Zhao Z.

Artif Intell Med. 2010 Feb-Mar;48(2-3):99-106. doi: 10.1016/j.artmed.2009.07.009.

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