Literature DB >> 17915856

Chemical data mining of the NCI human tumor cell line database.

Huijun Wang1, Jonathan Klinginsmith, Xiao Dong, Adam C Lee, Rajarshi Guha, Yuqing Wu, Gordon M Crippen, David J Wild.   

Abstract

The NCI Developmental Therapeutics Program Human Tumor cell line data set is a publicly available database that contains cellular assay screening data for over 40 000 compounds tested in 60 human tumor cell lines. The database also contains microarray assay gene expression data for the cell lines, and so it provides an excellent information resource particularly for testing data mining methods that bridge chemical, biological, and genomic information. In this paper we describe a formal knowledge discovery approach to characterizing and data mining this set and report the results of some of our initial experiments in mining the set from a chemoinformatics perspective.

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Year:  2007        PMID: 17915856     DOI: 10.1021/ci700141x

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  11 in total

1.  Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens.

Authors:  Pekka Tiikkainen; Antti Poso; Olli Kallioniemi
Journal:  J Comput Aided Mol Des       Date:  2008-12-03       Impact factor: 3.686

2.  Identification of compounds selectively killing multidrug-resistant cancer cells.

Authors:  Dóra Türk; Matthew D Hall; Benjamin F Chu; Joseph A Ludwig; Henry M Fales; Michael M Gottesman; Gergely Szakács
Journal:  Cancer Res       Date:  2009-10-20       Impact factor: 12.701

3.  Evaluation of molecular descriptors for antitumor drugs with respect to noncovalent binding to DNA and antiproliferative activity.

Authors:  José Portugal
Journal:  BMC Pharmacol       Date:  2009-09-16

4.  Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data.

Authors:  Bin Chen; Xiao Dong; Dazhi Jiao; Huijun Wang; Qian Zhu; Ying Ding; David J Wild
Journal:  BMC Bioinformatics       Date:  2010-05-17       Impact factor: 3.169

5.  Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules.

Authors:  Tiejun Cheng; Yanli Wang; Stephen H Bryant
Journal:  Bioinformatics       Date:  2010-10-13       Impact factor: 6.937

6.  WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications.

Authors:  Qian Zhu; Michael S Lajiness; Ying Ding; David J Wild
Journal:  J Cheminform       Date:  2010-08-20       Impact factor: 5.514

7.  Data mining the NCI60 to predict generalized cytotoxicity.

Authors:  Adam C Lee; Kerby Shedden; Gustavo R Rosania; Gordon M Crippen
Journal:  J Chem Inf Model       Date:  2008-06-28       Impact factor: 4.956

8.  Antiprotozoal, anticancer and antimicrobial activities of dihydroartemisinin acetal dimers and monomers.

Authors:  Desmond Slade; Ahmed M Galal; Waseem Gul; Mohamed M Radwan; Safwat A Ahmed; Shabana I Khan; Babu L Tekwani; Melissa R Jacob; Samir A Ross; Mahmoud A Elsohly
Journal:  Bioorg Med Chem       Date:  2009-10-30       Impact factor: 3.641

9.  Fast rule-based bioactivity prediction using associative classification mining.

Authors:  Pulan Yu; David J Wild
Journal:  J Cheminform       Date:  2012-11-23       Impact factor: 5.514

10.  A membrane transporter determines the spectrum of activity of a potent platinum-acridine hybrid anticancer agent.

Authors:  Xiyuan Yao; Noah H Watkins; Heather Brown-Harding; Ulrich Bierbach
Journal:  Sci Rep       Date:  2020-09-16       Impact factor: 4.379

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