Web-based tools for mining the NCI databases for anticancer drug discovery.
In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.
Fang
Xueliang
X
University of Michigan Comprehensive Cancer Center, Department of Internal Medicine, University of Michigan, 1500 E Medical Center Drive, Ann Arbor, Michigan 48109-0934, USA.
Shao
Lei
L
Zhang
Hui
H
Wang
Shaomeng
S
D000970:Antineoplastic Agents; D020407:Internet; D009316:National Institutes of Health (U.S.); D011493:Protein Kinase C / Q000378:metabolism; D014481:United States
D000970:Antineoplastic Agents; D011493:Protein Kinase C