Abstract：
:There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Fourches D,Muratov E,Tropsha Adoi
10.1021/acs.jcim.6b00129subject
Has Abstractpub_date
2016-07-25 00:00:00pages
1243-52issue
7eissn
1549-9596issn
1549-960Xjournal_volume
56pub_type
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