Literature DB >> 16846796

Methods for mining HTS data.

Gavin Harper1, Stephen D Pickett.   

Abstract

Data mining is a fast-growing field that is finding application across a wide range of industries. HTS is a crucial part of the drug discovery process at most large pharmaceutical companies. Accurate analysis of HTS data is, therefore, vital to drug discovery. Given the large quantity of data generated during an HTS, and the importance of analyzing those data effectively, it is unsurprising that data-mining techniques are now increasingly applied to HTS data analysis. Taking a broad view of both the HTS process and the data-mining process, we review recent literature that describes the application of data-mining techniques to HTS data.

Mesh:

Year:  2006        PMID: 16846796     DOI: 10.1016/j.drudis.2006.06.006

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  11 in total

1.  Analysis of high-throughput screening assays using cluster enrichment.

Authors:  Minya Pu; Tomoko Hayashi; Howard Cottam; Joseph Mulvaney; Michelle Arkin; Maripat Corr; Dennis Carson; Karen Messer
Journal:  Stat Med       Date:  2012-07-05       Impact factor: 2.373

2.  The development of a knowledge base for basic active structures: an example case of dopamine agonists.

Authors:  Takashi Okada; Masumi Yamakawa; Norihito Ohmori; Sachio Mori; Hiroshi Horikawa; Taketo Hayashi; Satoshi Fujishima
Journal:  Chem Cent J       Date:  2010-01-23       Impact factor: 4.215

3.  Extracting SAR Information from a Large Collection of Anti-Malarial Screening Hits by NSG-SPT Analysis.

Authors:  Mathias Wawer; Jürgen Bajorath
Journal:  ACS Med Chem Lett       Date:  2011-01-05       Impact factor: 4.345

4.  Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.

Authors:  Rong Liu; Robert Rallo; Saji George; Zhaoxia Ji; Sumitra Nair; André E Nel; Yoram Cohen
Journal:  Small       Date:  2011-03-24       Impact factor: 13.281

5.  Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles.

Authors:  Robert Rallo; Bryan France; Rong Liu; Sumitra Nair; Saji George; Robert Damoiseaux; Francesc Giralt; Andre Nel; Kenneth Bradley; Yoram Cohen
Journal:  Environ Sci Technol       Date:  2011-01-20       Impact factor: 9.028

6.  GPU accelerated chemical similarity calculation for compound library comparison.

Authors:  Chao Ma; Lirong Wang; Xiang-Qun Xie
Journal:  J Chem Inf Model       Date:  2011-07-01       Impact factor: 4.956

7.  An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries.

Authors:  Carol A Mulrooney; David L Lahr; Michael J Quintin; Willmen Youngsaye; Dennis Moccia; Jacob K Asiedu; Evan L Mulligan; Lakshmi B Akella; Lisa A Marcaurelle; Philip Montgomery; Joshua A Bittker; Paul A Clemons; Stephen Brudz; Sivaraman Dandapani; Jeremy R Duvall; Nicola J Tolliday; Andrea De Souza
Journal:  J Comput Aided Mol Des       Date:  2013-04-13       Impact factor: 3.686

Review 8.  The essential roles of chemistry in high-throughput screening triage.

Authors:  Jayme L Dahlin; Michael A Walters
Journal:  Future Med Chem       Date:  2014-07       Impact factor: 3.808

9.  QSAR workbench: automating QSAR modeling to drive compound design.

Authors:  Richard Cox; Darren V S Green; Christopher N Luscombe; Noj Malcolm; Stephen D Pickett
Journal:  J Comput Aided Mol Des       Date:  2013-04-25       Impact factor: 3.686

10.  poolHiTS: a shifted transversal design based pooling strategy for high-throughput drug screening.

Authors:  Raghunandan M Kainkaryam; Peter J Woolf
Journal:  BMC Bioinformatics       Date:  2008-05-30       Impact factor: 3.169

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