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Results: 1 to 20 of 64

1.

Computational cancer biology: education is a natural key to many locks.

Emmert-Streib F, Zhang SD, Hamilton P.

BMC Cancer. 2015 Jan 15;15:7. doi: 10.1186/s12885-014-1002-2.

2.

Comparative evaluation of gene set analysis approaches for RNA-Seq data.

Rahmatallah Y, Emmert-Streib F, Glazko G.

BMC Bioinformatics. 2014 Dec 5;15(1):397.

3.

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks.

Emmert-Streib F, Dehmer M, Haibe-Kains B.

Front Cell Dev Biol. 2014 Aug 19;2:38. doi: 10.3389/fcell.2014.00038. eCollection 2014. Review.

4.

Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.

Emmert-Streib F, Dehmer M, Haibe-Kains B.

Front Genet. 2014 Aug 29;5:299. doi: 10.3389/fgene.2014.00299. eCollection 2014.

5.

Functional and genetic analysis of the colon cancer network.

Emmert-Streib F, de Matos Simoes R, Glazko G, McDade S, Haibe-Kains B, Holzinger A, Dehmer M, Campbell F.

BMC Bioinformatics. 2014;15 Suppl 6:S6. doi: 10.1186/1471-2105-15-S6-S6. Epub 2014 May 16.

6.

Quantitative assessment and validation of network inference methods in bioinformatics.

Haibe-Kains B, Emmert-Streib F.

Front Genet. 2014 Jul 16;5:221. doi: 10.3389/fgene.2014.00221. eCollection 2014. No abstract available.

7.

Relevance of different prior knowledge sources for inferring gene interaction networks.

Olsen C, Bontempi G, Emmert-Streib F, Quackenbush J, Haibe-Kains B.

Front Genet. 2014 Jun 24;5:177. doi: 10.3389/fgene.2014.00177. eCollection 2014.

8.

NetBioV: an R package for visualizing large network data in biology and medicine.

Tripathi S, Dehmer M, Emmert-Streib F.

Bioinformatics. 2014 Oct;30(19):2834-6. doi: 10.1093/bioinformatics/btu384. Epub 2014 Jun 12.

PMID:
24928209
9.

Enhancing our understanding of ways to analyze metagenomes.

Emmert-Streib F.

Front Genet. 2014 Apr 29;5:108. doi: 10.3389/fgene.2014.00108. eCollection 2014. No abstract available.

10.

Interrelations of graph distance measures based on topological indices.

Dehmer M, Emmert-Streib F, Shi Y.

PLoS One. 2014 Apr 23;9(4):e94985. doi: 10.1371/journal.pone.0094985. eCollection 2014.

11.

Inference and validation of predictive gene networks from biomedical literature and gene expression data.

Olsen C, Fleming K, Prendergast N, Rubio R, Emmert-Streib F, Bontempi G, Haibe-Kains B, Quackenbush J.

Genomics. 2014 May-Jun;103(5-6):329-36. doi: 10.1016/j.ygeno.2014.03.004. Epub 2014 Mar 29.

12.

The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks.

Emmert-Streib F, de Matos Simoes R, Mullan P, Haibe-Kains B, Dehmer M.

Front Genet. 2014 Feb 3;5:15. doi: 10.3389/fgene.2014.00015. eCollection 2014.

13.

Netmes: assessing gene network inference algorithms by network-based measures.

Altay G, Kurt Z, Dehmer M, Emmert-Streib F.

Evol Bioinform Online. 2014 Feb 6;10:1-9. doi: 10.4137/EBO.S13481. eCollection 2014.

14.

Dry computational approaches for wet medical problems.

Emmert-Streib F, Zhang SD, Hamilton P.

J Transl Med. 2014 Jan 25;12:26. doi: 10.1186/1479-5876-12-26.

15.

Large-scale evaluation of molecular descriptors by means of clustering.

Dehmer M, Emmert-Streib F, Tripathi S.

PLoS One. 2013 Dec 31;8(12):e83956. doi: 10.1371/journal.pone.0083956. eCollection 2013.

16.

B-cell lymphoma gene regulatory networks: biological consistency among inference methods.

de Matos Simoes R, Dehmer M, Emmert-Streib F.

Front Genet. 2013 Dec 16;4:281. doi: 10.3389/fgene.2013.00281. eCollection 2013.

17.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too.

Emmert-Streib F, Dehmer M.

Front Genet. 2013 Nov 19;4:241. doi: 10.3389/fgene.2013.00241. eCollection 2013.

18.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets.

Rahmatallah Y, Emmert-Streib F, Glazko G.

Bioinformatics. 2014 Feb 1;30(3):360-8. doi: 10.1093/bioinformatics/btt687. Epub 2013 Nov 30.

19.

Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers.

Dehmer M, Mueller LA, Emmert-Streib F.

PLoS One. 2013 Nov 13;8(11):e77602. doi: 10.1371/journal.pone.0077602. eCollection 2013.

20.

[COMMODE] a large-scale database of molecular descriptors using compounds from PubChem.

Dander A, Mueller LA, Gallasch R, Pabinger S, Emmert-Streib F, Graber A, Dehmer M.

Source Code Biol Med. 2013 Nov 13;8(1):22. doi: 10.1186/1751-0473-8-22.

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