Evidence map›Paper›PMID 23320748›Full record

ArticleBMC systems biology2013

EnRICH: Extraction and Ranking using Integration and Criteria Heuristics.

Xia Zhang, M Heather West Greenlee, Jeanne M Serb

Abstract read
In one paragraph

Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xia ZhangDepartment of Biomedical Sciences, 2008 Veterinary Medicine, Iowa State University, Ames, IA 50010, USA.
M Heather West Greenlee
Jeanne M Serb

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh throughput screening technologies enable biologists to generate candidate genes at a rate that, due to time and cost constraints, cannot be studied by experimental approaches in the laboratory. Thus, it has become increasingly important to prioritize candidate genes for experiments. To accomplish this, researchers need to apply selection requirements based on their knowledge, which necessitates qualitative integration of heterogeneous data sources and filtration using multiple criteria. A similar approach can also be applied to putative candidate gene relationships. While automation can assist in this routine and imperative procedure, flexibility of data sources and criteria must not be sacrificed. A tool that can optimize the trade-off between automation and flexibility to simultaneously filter and qualitatively integrate data is needed to prioritize candidate genes and generate composite networks from heterogeneous data sources.

resultsWe developed the java application, EnRICH (Extraction and Ranking using Integration and Criteria Heuristics), in order to alleviate this need. Here we present a case study in which we used EnRICH to integrate and filter multiple candidate gene lists in order to identify potential retinal disease genes. As a result of this procedure, a candidate pool of several hundred genes was narrowed down to five candidate genes, of which four are confirmed retinal disease genes and one is associated with a retinal disease state.

conclusionsWe developed a platform-independent tool that is able to qualitatively integrate multiple heterogeneous datasets and use different selection criteria to filter each of them, provided the datasets are tables that have distinct identifiers (required) and attributes (optional). With the flexibility to specify data sources and filtering criteria, EnRICH automatically prioritizes candidate genes or gene relationships for biologists based on their specific requirements. Here, we also demonstrate that this tool can be effectively and easily used to apply highly specific user-defined criteria and can efficiently identify high quality candidate genes from relatively sparse datasets.

Indexed as

AlgorithmsModels, GeneticSoftwareGenetic Association StudiesHigh-Throughput Screening AssaysResearch Design

Identifiers

PMID23320748
PMCPMC3564850

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.