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Items: 1 to 20 of 108

1.

Integrated analysis identifies interaction patterns between small molecules and pathways.

Li Y, Li W, Chen X, Jiang H, Sun J, Chen H, Lv S.

Biomed Res Int. 2014;2014:931825. doi: 10.1155/2014/931825. Epub 2014 Jul 13.

2.

Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures.

Lee H, Kang S, Kim W.

PLoS One. 2016 Mar 8;11(3):e0150460. doi: 10.1371/journal.pone.0150460. eCollection 2016.

4.

An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors.

Huang L, Zhao S, Frasor JM, Dai Y.

PLoS One. 2011;6(7):e22274. doi: 10.1371/journal.pone.0022274. Epub 2011 Jul 15.

5.

CDA: combinatorial drug discovery using transcriptional response modules.

Lee JH, Kim DG, Bae TJ, Rho K, Kim JT, Lee JJ, Jang Y, Kim BC, Park KM, Kim S.

PLoS One. 2012;7(8):e42573. doi: 10.1371/journal.pone.0042573. Epub 2012 Aug 8.

6.

In silico identification of potential targets and drugs for non-small cell lung cancer.

Huang CH, Wu MY, Chang PM, Huang CY, Ng KL.

IET Syst Biol. 2014 Apr;8(2):56-66. doi: 10.1049/iet-syb.2013.0035.

PMID:
25014226
7.

Artemisinin rewires the protein interaction network in cancer cells: network analysis, pathway identification, and target prediction.

Huang C, Ba Q, Yue Q, Li J, Li J, Chu R, Wang H.

Mol Biosyst. 2013 Dec;9(12):3091-100. doi: 10.1039/c3mb70342h. Epub 2013 Oct 2.

PMID:
24085322
8.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

Ravindranath AC, Perualila-Tan N, Kasim A, Drakakis G, Liggi S, Brewerton SC, Mason D, Bodkin MJ, Evans DA, Bhagwat A, Talloen W, Göhlmann HW, Shkedy Z, Bender A; QSTAR Consortium.

Mol Biosyst. 2015 Jan;11(1):86-96. doi: 10.1039/c4mb00328d. Epub 2014 Sep 25.

PMID:
25254964
9.
10.

Gene expression profile and functional analysis of Alzheimer's disease.

Chen F, Guan Q, Nie ZY, Jin LJ.

Am J Alzheimers Dis Other Demen. 2013 Nov;28(7):693-701. doi: 10.1177/1533317513500838. Epub 2013 Sep 4.

PMID:
24005853
11.

Cliques for the identification of gene signatures for colorectal cancer across population.

Pradhan MP, Nagulapalli K, Palakal MJ.

BMC Syst Biol. 2012;6 Suppl 3:S17. doi: 10.1186/1752-0509-6-S3-S17. Epub 2012 Dec 17.

12.

Molecular concepts analysis links tumors, pathways, mechanisms, and drugs.

Rhodes DR, Kalyana-Sundaram S, Tomlins SA, Mahavisno V, Kasper N, Varambally R, Barrette TR, Ghosh D, Varambally S, Chinnaiyan AM.

Neoplasia. 2007 May;9(5):443-54.

13.

Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer.

Stegmaier P, Voss N, Meier T, Kel A, Wingender E, Borlak J.

PLoS One. 2011 Mar 28;6(3):e17738. doi: 10.1371/journal.pone.0017738.

14.

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.

Huang YF, Yeh HY, Soo VW.

BMC Med Genomics. 2013;6 Suppl 3:S4. doi: 10.1186/1755-8794-6-S3-S4. Epub 2013 Nov 11.

15.

A computational bioinformatics analysis of gene expression identifies candidate agents for prostate cancer.

Wen DY, Geng J, Li W, Guo CC, Zheng JH.

Andrologia. 2014 Aug;46(6):625-32. doi: 10.1111/and.12127. Epub 2013 Jun 24.

PMID:
23790256
16.

Convergence and divergence of genetic and modular networks between diabetes and breast cancer.

Zhang X, Zhang Y, Yu Y, Liu J, Yuan Y, Zhao Y, Li H, Wang J, Wang Z.

J Cell Mol Med. 2015 May;19(5):1094-102. doi: 10.1111/jcmm.12504. Epub 2015 Mar 6.

17.

Pathway-specific differences between tumor cell lines and normal and tumor tissue cells.

Ertel A, Verghese A, Byers SW, Ochs M, Tozeren A.

Mol Cancer. 2006 Nov 2;5(1):55.

18.

Applications of Connectivity Map in drug discovery and development.

Qu XA, Rajpal DK.

Drug Discov Today. 2012 Dec;17(23-24):1289-98. doi: 10.1016/j.drudis.2012.07.017. Epub 2012 Aug 5. Review.

PMID:
22889966
19.

Comprehensive transcriptomic analysis of molecularly targeted drugs in cancer for target pathway evaluation.

Mashima T, Ushijima M, Matsuura M, Tsukahara S, Kunimasa K, Furuno A, Saito S, Kitamura M, Soma-Nagae T, Seimiya H, Dan S, Yamori T, Tomida A.

Cancer Sci. 2015 Jul;106(7):909-20. doi: 10.1111/cas.12682. Epub 2015 May 25.

20.

Computational analysis of mRNA expression profiles identifies the ITG family and PIK3R3 as crucial genes for regulating triple negative breast cancer cell migration.

Klahan S, Wu MS, Hsi E, Huang CC, Hou MF, Chang WC.

Biomed Res Int. 2014;2014:536591. doi: 10.1155/2014/536591. Epub 2014 May 6.

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