Mining and Visualizing Large Anticancer Drug Discovery Databases

Content Provider	Semantic Scholar
Author	Shi, Leming Fan, Yi Lee, Jae K. Waltham, Mark Andrews, Darren T. Scherf, Uwe Paull, Kenneth D. Weinstein, John N.
Copyright Year	2000
Abstract	In order to find more effective anticancer drugs, the U.S. National Cancer Institute (NCI) screens a large number of compounds in vitro against 60 human cancer cell lines from different organs of origin. About 70,000 compounds have been tested in the program since 1990, and each tested compound can be characterized by a vector (i.e., "fingerprint") of 60 anticancer activity, or -[log(GI50)], values. GI50 is the concentration required to inhibit cell growth by 50% compared with untreated controls. Although cell growth inhibitory activity for a single cell line is not very informative, activity patterns across the 60 cell lines can provide incisive information on the mechanisms of action of screened compounds and also on molecular targets and modulators of activity within the cancer cells. Various statistical and artificial intelligence methods, including principal component analysis, hierarchical cluster analysis, stepwise linear regression, multidimensional scaling, neural network modeling, and genetic function approximation, among others, can be used to analyze this large activity database. Mining the database can provide useful information: (a) for the development of anticancer drugs; (b) for a better understanding of the molecular pharmacology of cancer; and (c) for improvement of the drug discovery process.
Starting Page	48
Ending Page	65
Page Count	18
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.wikidata.org/entity/Q40887596
Alternate Webpage(s)	http://www.ctu.edu.vn/~dvxe/Bioinformatic/gene-chips/CancerDataMining.pdf
PubMed reference number	10761142v1
Alternate Webpage(s)	https://doi.org/10.1021/ci990087b
DOI	10.1021/ci990087b
Journal	Journal of Chemical Information and Computer Sciences
Volume Number	40
Issue Number	2
Language	English
Access Restriction	Open
Subject Keyword	Approximation Artificial intelligence Artificial neural network Cluster analysis Cultured Cell Line Drug Discovery Emoticon Fingerprint Hierarchical clustering Incisive Information Linear IgA Bullous Dermatosis Multidimensional scaling NC (complexity) National Cancer Institute Neoplasms Neural Network Simulation Organ Pharmacology Principal component analysis Projection screen Stepwise regression Vector graphics cancer cell cell growth
Content Type	Text
Resource Type	Article