A statistical approach to identify, monitor, and manage incomplete curated data sets.

ABSTRACT:

Background

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here.

Results

In this work, a multivariate linear regression model was used to identify genes in the Zebraf

SUBMITTER: Howe DG 

PROVIDER: S-EPMC5879614 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature