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Items: 1 to 20 of 171

1.

Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo.

Field SL, Dasgupta T, Cummings M, Savage RS, Adebayo J, McSara H, Gunawardena J, Orsi NM.

BMC Syst Biol. 2015 Nov 9;9:76. doi: 10.1186/s12918-015-0226-3.

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3.

Virus-encoded microRNA contributes to the molecular profile of EBV-positive Burkitt lymphomas.

Piccaluga PP, Navari M, De Falco G, Ambrosio MR, Lazzi S, Fuligni F, Bellan C, Rossi M, Sapienza MR, Laginestra MA, Etebari M, Rogena EA, Tumwine L, Tripodo C, Gibellini D, Consiglio J, Croce CM, Pileri SA, Leoncini L.

Oncotarget. 2016 Jan 5;7(1):224-40. doi: 10.18632/oncotarget.4399.

4.

Bioinformatics analysis of potential essential genes that response to the high intraocular pressure on astrocyte due to glaucoma.

Yang Y, Duan JZ, Di Y, Gui DM, Gao DW.

Int J Ophthalmol. 2015 Apr 18;8(2):395-8. doi: 10.3980/j.issn.2222-3959.2015.02.32. eCollection 2015.

5.

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

Bravo À, Piñero J, Queralt-Rosinach N, Rautschka M, Furlong LI.

BMC Bioinformatics. 2015 Feb 21;16:55. doi: 10.1186/s12859-015-0472-9.

6.

ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology.

Ivanisenko VA, Saik OV, Ivanisenko NV, Tiys ES, Ivanisenko TV, Demenkov PS, Kolchanov NA.

BMC Syst Biol. 2015;9 Suppl 2:S2. doi: 10.1186/1752-0509-9-S2-S2. Epub 2015 Apr 15.

7.

Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome).

Glotov AS, Tiys ES, Vashukova ES, Pakin VS, Demenkov PS, Saik OV, Ivanisenko TV, Arzhanova ON, Mozgovaya EV, Zainulina MS, Kolchanov NA, Baranov VS, Ivanisenko VA.

BMC Syst Biol. 2015;9 Suppl 2:S4. doi: 10.1186/1752-0509-9-S2-S4. Epub 2015 Apr 15.

8.

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

Hur J, Özgür A, Xiang Z, He Y.

J Biomed Semantics. 2015 Jan 6;6:2. doi: 10.1186/2041-1480-6-2. eCollection 2015.

9.

Open source libraries and frameworks for biological data visualisation: a guide for developers.

Wang R, Perez-Riverol Y, Hermjakob H, Vizcaíno JA.

Proteomics. 2015 Apr;15(8):1356-74. doi: 10.1002/pmic.201400377. Epub 2015 Feb 5. Review.

10.

Longitudinal study of circulating protein biomarkers in inflammatory bowel disease.

Viennois E, Baker MT, Xiao B, Wang L, Laroui H, Merlin D.

J Proteomics. 2015 Jan 1;112:166-79. doi: 10.1016/j.jprot.2014.09.002. Epub 2014 Sep 16.

11.

Excretion of urinary orosomucoid 1 protein is elevated in patients with chronic heart failure.

Hou LN, Li F, Zeng QC, Su L, Chen PA, Xu ZH, Zhu DJ, Liu CH, Xu DL.

PLoS One. 2014 Sep 12;9(9):e107550. doi: 10.1371/journal.pone.0107550. eCollection 2014.

12.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

Qin T, Matmati N, Tsoi LC, Mohanty BK, Gao N, Tang J, Lawson AB, Hannun YA, Zheng WJ.

Nucleic Acids Res. 2014 Oct;42(18):e138. doi: 10.1093/nar/gku678. Epub 2014 Jul 24.

13.

Potential therapeutic targets for oral cancer: ADM, TP53, EGFR, LYN, CTLA4, SKIL, CTGF, CD70.

Bundela S, Sharma A, Bisen PS.

PLoS One. 2014 Jul 16;9(7):e102610. doi: 10.1371/journal.pone.0102610. eCollection 2014.

14.

The Epstein Barr-encoded BART-6-3p microRNA affects regulation of cell growth and immuno response in Burkitt lymphoma.

Ambrosio MR, Navari M, Di Lisio L, Leon EA, Onnis A, Gazaneo S, Mundo L, Ulivieri C, Gomez G, Lazzi S, Piris MA, Leoncini L, De Falco G.

Infect Agent Cancer. 2014 Apr 14;9:12. doi: 10.1186/1750-9378-9-12. eCollection 2014.

15.

Prioritizing protein complexes implicated in human diseases by network optimization.

Chen Y, Jacquemin T, Zhang S, Jiang R.

BMC Syst Biol. 2014;8 Suppl 1:S2. doi: 10.1186/1752-0509-8-S1-S2. Epub 2014 Jan 24.

16.

A nucleosomal approach to inferring causal relationships of histone modifications.

Le N, Ho T, Ho B, Tran D.

BMC Genomics. 2014;15 Suppl 1:S7. doi: 10.1186/1471-2164-15-S1-S7. Epub 2014 Jan 24.

17.

A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.

Xiang Z, Qin T, Qin ZS, He Y.

BMC Syst Biol. 2013 Oct 16;7 Suppl 3:S9. doi: 10.1186/1752-0509-7-S3-S9.

18.

Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM.

PLoS One. 2013 Dec 2;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013.

19.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology.

Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J.

OMICS. 2013 Dec;17(12):595-610. doi: 10.1089/omi.2013.0017. Epub 2013 Oct 12. Review.

20.

CoCiter: an efficient tool to infer gene function by assessing the significance of literature co-citation.

Qiao N, Huang Y, Naveed H, Green CD, Han JD.

PLoS One. 2013 Sep 23;8(9):e74074. doi: 10.1371/journal.pone.0074074. eCollection 2013.

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