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Items: 1 to 20 of 154

1.

An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

Nibbe RK, Koyutürk M, Chance MR.

PLoS Comput Biol. 2010 Jan 15;6(1):e1000639. doi: 10.1371/journal.pcbi.1000639.

2.

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.

PMID:
22000346
4.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer.

Xu M, Kao MC, Nunez-Iglesias J, Nevins JR, West M, Zhou XJ.

BMC Genomics. 2008;9 Suppl 1:S12. doi: 10.1186/1471-2164-9-S1-S12.

5.

Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling.

Kulkarni YM, Suarez V, Klinke DJ 2nd.

BMC Cancer. 2010 Jun 15;10:291. doi: 10.1186/1471-2407-10-291.

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7.

Mining functional subgraphs from cancer protein-protein interaction networks.

Shen R, Goonesekere NC, Guda C.

BMC Syst Biol. 2012;6 Suppl 3:S2. doi: 10.1186/1752-0509-6-S3-S2. Epub 2012 Dec 17.

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10.

Metastatic susceptibility locus, an 8p hot-spot for tumour progression disrupted in colorectal liver metastases: 13 candidate genes examined at the DNA, mRNA and protein level.

Macartney-Coxson DP, Hood KA, Shi HJ, Ward T, Wiles A, O'Connor R, Hall DA, Lea RA, Royds JA, Stubbs RS, Rooker S.

BMC Cancer. 2008 Jul 1;8:187. doi: 10.1186/1471-2407-8-187.

11.

PPI spider: a tool for the interpretation of proteomics data in the context of protein-protein interaction networks.

Antonov AV, Dietmann S, Rodchenkov I, Mewes HW.

Proteomics. 2009 May;9(10):2740-9. doi: 10.1002/pmic.200800612.

PMID:
19405022
12.

Global alterations in mRNA polysomal recruitment in a cell model of colorectal cancer progression to metastasis.

Provenzani A, Fronza R, Loreni F, Pascale A, Amadio M, Quattrone A.

Carcinogenesis. 2006 Jul;27(7):1323-33. Epub 2006 Mar 10.

PMID:
16531451
13.

Mixture classification model based on clinical markers for breast cancer prognosis.

Zeng T, Liu J.

Artif Intell Med. 2010 Feb-Mar;48(2-3):129-37. doi: 10.1016/j.artmed.2009.07.008. Epub 2009 Dec 14.

PMID:
20005686
14.

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

Phan JH, Quo CF, Wang MD.

Prog Brain Res. 2006;158:83-108. Review.

PMID:
17027692
15.

Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data.

Qian J, Lin J, Luscombe NM, Yu H, Gerstein M.

Bioinformatics. 2003 Oct 12;19(15):1917-26.

PMID:
14555624
16.

Quantitative proteomic and genomic profiling reveals metastasis-related protein expression patterns in gastric cancer cells.

Chen YR, Juan HF, Huang HC, Huang HH, Lee YJ, Liao MY, Tseng CW, Lin LL, Chen JY, Wang MJ, Chen JH, Chen YJ.

J Proteome Res. 2006 Oct;5(10):2727-42.

PMID:
17022644
17.
18.

Transcriptomic and Proteomic Data Integration and Two-Dimensional Molecular Maps with Regulatory and Functional Linkages: Application to Cell Proliferation and Invasion Networks in Glioblastoma.

Gupta MK, Jayaram S, Reddy DN, Polisetty RV, Sirdeshmukh R.

J Proteome Res. 2015 Dec 4;14(12):5017-27. doi: 10.1021/acs.jproteome.5b00765. Epub 2015 Oct 23.

PMID:
26464075
19.

Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

Torres-García W, Zhang W, Runger GC, Johnson RH, Meldrum DR.

Bioinformatics. 2009 Aug 1;25(15):1905-14. doi: 10.1093/bioinformatics/btp325. Epub 2009 May 15.

20.

MicroRNA expression profiles in human colorectal cancers with liver metastases.

Lin M, Chen W, Huang J, Gao H, Ye Y, Song Z, Shen X.

Oncol Rep. 2011 Mar;25(3):739-47. doi: 10.3892/or.2010.1112. Epub 2010 Dec 20.

PMID:
21174058

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