|
1
|
Keiser MJ, Setola V, Irwin JJ, Laggner C,
Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, et
al: Predicting new molecular targets for known drugs. Nature.
462:175–181. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Kibble M, Saarinen N, Tang J, Wennerberg
K, Mäkelä S and Aittokallio T: Network pharmacology applications to
map the unexplored target space and therapeutic potential of
natural products. Nat Prod Rep. 32:1249–1266. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Lin J, Wu L, Bai X, Xie Y, Wang A, Zhang
H, Yang X, Wan X, Lu X, Sang X and Zhao H: Combination treatment
including targeted therapy for advanced hepatocellular carcinoma.
Oncotarget. 7:71036–71051. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Hopkins AL: Network pharmacology: The next
paradigm in drug discovery. Nat Chem Biol. 4:682–690. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Lamb J, Crawford ED, Peck D, Modell JW,
Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et
al: The Connectivity Map: Using gene-expression signatures to
connect small molecules, genes, and disease. Science.
313:1929–1935. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Qu XA and Rajpal DK: Applications of
Connectivity Map in drug discovery and development. Drug Discov
Today. 17:1289–1298. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Leung EL, Cao ZW, Jiang ZH, Zhou H and Liu
L: Network-based drug discovery by integrating systems biology and
computational technologies. Brief Bioinform. 14:491–505. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Zhang QY, Chu XY, Jiang LH, Liu MY, Mei ZL
and Zhang HY: Identification of non-electrophilic Nrf2 activators
from approved drugs. Molecules. 22:pii: E883. 2017.
|
|
9
|
Chung FH, Chiang YR, Tseng AL, Sung YC, Lu
J, Huang MC, Ma N and Lee HC: Functional module Connectivity Map
(FMCM): A framework for searching repurposed drug compounds for
systems treatment of cancer and an application to colorectal
adenocarcinoma. PLoS One. 9:e862992014. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Zhou W, Ma CX, Xing YZ and Yan ZY:
Identification of candidate target genes of pituitary adenomas
based on the DNA microarray. Mol Med Rep. 13:2182–2186. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Iskar M, Zeller G, Blattmann P, Campillos
M, Kuhn M, Kaminska KH, Runz H, Gavin AC, Pepperkok R, van Noort V
and Bork P: Characterization of drug-induced transcriptional
modules: Towards drug repositioning and functional understanding.
Mol Syst Biol. 9:6622013. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Raghavan R, Hyter S, Pathak HB, Godwin AK,
Konecny G, Wang C, Goode EL and Fridley BL: Drug discovery using
clinical outcome-based Connectivity Mapping: Application to ovarian
cancer. BMC Genomics. 17:8112016. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Cheng J, Yang L, Kumar V and Agarwal P:
Systematic evaluation of Connectivity Map for disease indications.
Genome Med. 6:5402014. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Patil P, Bachant-Winner PO, Haibe-Kains B
and Leek JT: Test set bias affects reproducibility of gene
signatures. Bioinformatics. 31:2318–2323. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Chibon F: Cancer gene expression
signatures-the rise and fall? Eur J Cancer. 49:2000–2009. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Ivliev AE, t Hoen PA and Sergeeva MG:
Coexpression network analysis identifies transcriptional modules
related to proastrocytic differentiation and sprouty signaling in
glioma. Cancer Res. 70:10060–10070. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Zhang B and Horvath S: A general framework
for weighted gene co-expression network analysis. Stat Appl Genet
Mol Biol. 4:Article172005. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Langfelder P, Zhang B and Horvath S:
Defining clusters from a hierarchical cluster tree: The dynamic
tree cut package for R. Bioinformatics. 24:719–720. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
da Huang W, Sherman BT and Lempicki RA:
Bioinformatics enrichment tools: Paths toward the comprehensive
functional analysis of large gene lists. Nucleic Acids Res.
37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Liu W, Li L and Li W: Gene co-expression
analysis identifies common modules related to prognosis and drug
resistance in cancer cell lines. Int J Cancer. 135:2795–2803. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Napolitano F, Sirci F, Carrella D and di
Bernardo D: Drug-set enrichment analysis: A novel tool to
investigate drug mode of action. Bioinformatics. 32:235–241.
2016.PubMed/NCBI
|
|
22
|
Brandes LJ, Warrington RC, Arron RJ,
Bogdanovic RP, Fang W, Queen GM, Stein DA, Tong J, Zaborniak CL and
LaBella FS: Enhanced cancer growth in mice administered daily
human-equivalent doses of some H1-antihistamines: predictive in
vitro correlates. J Natl Cancer Inst. 86:770–775. 1994. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
de Guadalupe Chávez-López M,
Hernández-Gallegos E, Vázquez-Sánchez AY, Gariglio P and Camacho J:
Antiproliferative and proapoptotic effects of astemizole on
cervical cancer cells. Int J Gynecol Cancer. 24:824–828. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Velicer CM, Heckbert SR, Lampe JW, Potter
JD, Robertson CA and Taplin SH: Antibiotic use in relation to the
risk of breast cancer. JAMA. 291:827–835. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Hwang KA, Park MA, Kang NH, Yi BR, Hyun
SH, Jeung EB and Choi KC: Anticancer effect of genistein on BG-1
ovarian cancer growth induced by 17 β-estradiol or bisphenol A via
the suppression of the crosstalk between estrogen receptor α and
insulin-like growth factor-1 receptor signaling pathways. Toxicol
Appl Pharmacol. 272:637–646. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Dubé C, Rostom A, Lewin G, Tsertsvadze A,
Barrowman N, Code C, Sampson M and Moher D: U.S. Preventive
Services Task Force: The use of aspirin for primary prevention of
colorectal cancer: a systematic review prepared for the U.S.
preventive services task force. Ann Intern Med. 146:365–375. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Brambilla G, Mattioli F, Robbiano L and
Martelli A: Studies on genotoxicity and carcinogenicity of
antibacterial, antiviral, antimalarial and antifungal drugs.
Mutagenesis. 27:387–413. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Weisburger JH, Shirasu Y, Grantham PH and
Weisburger EK: Chloramphenicol, protein synthesis, and the
metabolism of the carcinogen N-2-fluorenyldiacetamide in rats.
Inhibition by chloramphenicol of carcinogen binding. J Biol Chem.
242:372–378. 1967.PubMed/NCBI
|
|
29
|
Kidder GW, Dewey VC and Parks RE Jr:
Effect of lowered essential metabolites on 8-azaguanine inhibition.
J Biol Chem. 197:193–198. 1952.PubMed/NCBI
|
|
30
|
Sugiura K, Hitchings GH, Cavalieri LF and
Stock CC: The effect of 8-azaguanine on the growth of carcinoma,
sarcoma, osteogenic sarcoma, lymphosarcoma and melanoma in animals.
Cancer Res. 10:178–185. 1950.PubMed/NCBI
|
|
31
|
Ye H and Liu W: Transcriptional networks
implicated in human nonalcoholic fatty liver disease. Mol Genet
Genomics. 290:1793–1804. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Menacho-Márquez M, Nogueiras R, Fabbiano
S, Sauzeau V, Al-Massadi O, Diéguez C and Bustelo XR: Chronic
sympathoexcitation through loss of Vav3, a Rac1 activator, results
in divergent effects on metabolic syndrome and obesity depending on
diet. Cell Metab. 18:199–211. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Lassus A, Forström S and Salo O: A
double-blind comparison of sulconazole nitrate 1% cream with
clotrimazole 1% cream in the treatment of dermatophytoses. Brit J
Dermatol. 108:195–198. 1983. View Article : Google Scholar
|
|
34
|
Barabási AL, Gulbahce N and Loscalzo J:
Network medicine: A network-based approach to human disease. Nat
Rev Genet. 12:56–68. 2011. View
Article : Google Scholar : PubMed/NCBI
|
|
35
|
Wu Z, Wang Y and Chen L: Network-based
drug repositioning. Mol Biosyst. 9:1268–1281. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Park S, Lee DG and Shin H: Network
mirroring for drug repositioning. BMC Med Inform Decis Mak. 17
Suppl 1:S552017. View Article : Google Scholar
|
|
37
|
Gottlieb A, Stein GY, Ruppin E and Sharan
R: PREDICT: A method for inferring novel drug indications with
application to personalized medicine. Mol Syst Biol. 7:4962011.
View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Chen H, Zhang H, Zhang Z, Cao Y and Tang
W: Network-based inference methods for drug repositioning. Comput
Math Methods Med. 2015:1306202015. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Luo Y, Zhao X, Zhou J, Yang J, Zhang Y,
Kuang W, Peng J, Chen L and Zeng J: A network integration approach
for drug-target interaction prediction and computational drug
repositioning from heterogeneous information. Nat Commun.
8:5732017. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Li YY and Jones SJ: Drug repositioning for
personalized medicine. Genome Med. 4:272012.PubMed/NCBI
|
