Dataset Information

GLAD4U: deriving and prioritizing gene lists from PubMed literature.

ABSTRACT: Answering questions such as "Which genes are related to breast cancer?" usually requires retrieving relevant publications through the PubMed search engine, reading these publications, and creating gene lists. This process is not only time-consuming, but also prone to errors.We report GLAD4U (Gene List Automatically Derived For You), a new, free web-based gene retrieval and prioritization tool. GLAD4U takes advantage of existing resources of the NCBI to ensure computational efficiency. The quality of gene lists created by GLAD4U for three Gene Ontology (GO) terms and three disease terms was assessed using corresponding "gold standard" lists curated in public databases. For all queries, GLAD4U gene lists showed very high recall but low precision, leading to low F-measure. As a comparison, EBIMed's recall was consistently lower than GLAD4U, but its precision was higher. To present the most relevant genes at the top of a list, we studied two prioritization methods based on publication count and the hypergeometric test, and compared the ranked lists and those generated by EBIMed to the gold standards. Both GLAD4U methods outperformed EBIMed for all queries based on a variety of quality metrics. Moreover, the hypergeometric method allowed for a better performance by thresholding genes with low scores. In addition, manual examination suggests that many false-positives could be explained by the incompleteness of the gold standards. The GLAD4U user interface accepts any valid queries for PubMed, and its output page displays the ranked gene list and information associated with each gene, chronologically-ordered supporting publications, along with a summary of the run and links for file export and functional enrichment and protein interaction network analysis.GLAD4U has a high overall recall. Although precision is generally low, the prioritization methods successfully rank truly relevant genes at the top of the lists to facilitate efficient browsing. GLAD4U is simple to use, and its interface can be found at: http://bioinfo.vanderbilt.edu/glad4u.

SUBMITTER: Jourquin J

PROVIDER: S-EPMC3535723 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GLAD4U: deriving and prioritizing gene lists from PubMed literature.

Jourquin Jérôme J Duncan Dexter D Shi Zhiao Z Zhang Bing B

BMC genomics 20121217

<h4>Background</h4>Answering questions such as "Which genes are related to breast cancer?" usually requires retrieving relevant publications through the PubMed search engine, reading these publications, and creating gene lists. This process is not only time-consuming, but also prone to errors.<h4>Results</h4>We report GLAD4U (Gene List Automatically Derived For You), a new, free web-based gene retrieval and prioritization tool. GLAD4U takes advantage of existing resources of the NCBI to ensure c ...[more]

PMID: 23282288

Similar Datasets

Project description:Active management of existing health technologies (e.g., devices, diagnostic, and/or medical procedures) to ensure the delivery of high value care is increasingly recognized around the world. A number of initiatives have raised awareness of technologies that may be overused, mis-used, or potentially harmful by compiling them into lists of low value care. However, despite the growing number of lists, changes to local healthcare practices remain challenging for many systems. The objective of this study was to develop and implement a process, leveraging existing initiatives and data assets, to produce a list of prioritized low value technologies for health technology reassessment (HTR).An expert advisory committee comprised of clinical experts and health system decision-makers was convened to determine key process requirements. Once developed, the process was piloted to assess feasibility in the Canadian province of British Columbia (BC).The expert advisory committee identified five required attributes for the process: data-driven, routine and replicable, actionable, stakeholder collaboration, and high return on investment. Guided by these attributes, a 5-step process was developed. First, over 1300 published low value technologies (i.e., from the National Institute for Health and Care Excellence [NICE] "do not do" recommendations, low value technologies in the Australian Medical Benefits Schedule, and Choosing Wisely "Top 5" lists) were identified. Using appropriate coding systems for BC's administrative health data (e.g., International Classification of Diseases [ICD]), the low value technologies were queried to examine frequencies and costs of technology use. This information was used to rank potential candidates for reassessment based on high annual budgetary impact. Lastly, clinical experts reviewed the ranked technologies prior to broad dissemination and stakeholder action. Pilot testing of the process in BC resulted in the prioritization of 9 initial candidate technologies for reassessment.This is the first account of a systematic approach to move a collective body of low value technology recommendations into action in a healthcare setting. This work demonstrates the feasibility and strength of using administrative data to identify and prioritize low value technologies for HTR at a population-level.

Dataset Information

GLAD4U: deriving and prioritizing gene lists from PubMed literature.

Publications

GLAD4U: deriving and prioritizing gene lists from PubMed literature.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets