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Items: 1 to 20 of 39


MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.

Ayati M, Erten S, Chance MR, Koyut├╝rk M.

EURASIP J Bioinform Syst Biol. 2015 Jun 30;2015:7. doi: 10.1186/s13637-015-0025-6.


WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Chen X, Yan CC, Zhang X, You ZH, Deng L, Liu Y, Zhang Y, Dai Q.

Sci Rep. 2016 Feb 16;6:21106. doi: 10.1038/srep21106.


Pathway and network approaches for identification of cancer signature markers from omics data.

Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW.

J Cancer. 2015 Jan 1;6(1):54-65. doi: 10.7150/jca.10631. Review.


Text mining in cancer gene and pathway prioritization.

Luo Y, Riedlinger G, Szolovits P.

Cancer Inform. 2014 Oct 13;13(Suppl 1):69-79. doi: 10.4137/CIN.S13874. Review.


Associating disease-related genetic variants in intergenic regions to the genes they impact.

Macintyre G, Jimeno Yepes A, Ong CS, Verspoor K.

PeerJ. 2014 Oct 23;2:e639. doi: 10.7717/peerj.639.


Bioinformatics for personal genome interpretation.

Capriotti E, Nehrt NL, Kann MG, Bromberg Y.

Brief Bioinform. 2012 Jul;13(4):495-512. Review.


Elucidating the identity of resistance mechanisms to prednisolone exposure in acute lymphoblastic leukemia cells through transcriptomic analysis: A computational approach.

Sifakis EG, Lambrou GI, Prentza A, Vlahopoulos S, Koutsouris D, Tzortzatou-Stathopoulou F, Chatziioannou AA.

J Clin Bioinforma. 2011 Dec 20;1:36. doi: 10.1186/2043-9113-1-36.


Bioinformatics challenges for personalized medicine.

Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB.

Bioinformatics. 2011 Jul 1;27(13):1741-8. doi: 10.1093/bioinformatics/btr295. Erratum in: Bioinformatics. 2011 Aug 15;27(16):2323.


BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs.

Lin FP, Anthony S, Polasek TM, Tsafnat G, Doogue MP.

BMC Bioinformatics. 2011 Apr 21;12:112. doi: 10.1186/1471-2105-12-112.


TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery.

Chen YA, Tripathi LP, Mizuguchi K.

PLoS One. 2011 Mar 8;6(3):e17844. doi: 10.1371/journal.pone.0017844.


Prioritization of disease microRNAs through a human phenome-microRNAome network.

Jiang Q, Hao Y, Wang G, Juan L, Zhang T, Teng M, Liu Y, Wang Y.

BMC Syst Biol. 2010 May 28;4 Suppl 1:S2. doi: 10.1186/1752-0509-4-S1-S2.


PhenoHM: human-mouse comparative phenome-genome server.

Sardana D, Vasa S, Vepachedu N, Chen J, Gudivada RC, Aronow BJ, Jegga AG.

Nucleic Acids Res. 2010 Jul;38(Web Server issue):W165-74. doi: 10.1093/nar/gkq472.


Bioinformatic tools for identifying disease gene and SNP candidates.

Mooney SD, Krishnan VG, Evani US.

Methods Mol Biol. 2010;628:307-19. doi: 10.1007/978-1-60327-367-1_17. Review.


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.


Gene prioritization and clustering by multi-view text mining.

Yu S, Tranchevent LC, De Moor B, Moreau Y.

BMC Bioinformatics. 2010 Jan 14;11:28. doi: 10.1186/1471-2105-11-28.


Biomedical text mining and its applications.

Rodriguez-Esteban R.

PLoS Comput Biol. 2009 Dec;5(12):e1000597. doi: 10.1371/journal.pcbi.1000597. No abstract available.


Annotating the human genome with Disease Ontology.

Osborne JD, Flatow J, Holko M, Lin SM, Kibbe WA, Zhu LJ, Danila MI, Feng G, Chisholm RL.

BMC Genomics. 2009 Jul 7;10 Suppl 1:S6. doi: 10.1186/1471-2164-10-S1-S6.


Gendoo: functional profiling of gene and disease features using MeSH vocabulary.

Nakazato T, Bono H, Matsuda H, Takagi T.

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W166-9. doi: 10.1093/nar/gkp483.


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.

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