Automated Assessment of Data Quality in Biological Knowledge Resources

Abstract

This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approach based on network analysis of database record connectivity. These tools will enable quantifying data quality at scale. Researchers, evidence-based decision-makers in biomedicine, and the analytical or predictive tools that use this data will make more reliable inference..

Related publications (1)

Automatic consistency assurance for literature-based gene ontology annotation

Jiyu Chen, Nicholas Geard, Justin Zobel, Karin Verspoor

2021-11-25

BACKGROUND: Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe ..