%0 Journal Article
%T EnRICH: Extraction and Ranking using Integration and Criteria Heuristics
%A Xia Zhang
%A M Heather West Greenlee
%A Jeanne M Serb
%J BMC Systems Biology
%D 2013
%I BioMed Central
%R 10.1186/1752-0509-7-4
%X We 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.We 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.Hundreds to thousands of candidate genes, or genes of interest, can now be generated from a single experiment utilizing high throughput screening technologies. However, the number of candidate genes that can be experimentally studied in-depth is often constrained by time and cost. Therefore, prioritization of candidate genes is a critical step in the experimental process. Approaches to identify ¡®the most promising¡¯ candidates are becoming increasingly more sophisticated. For example, when microarray studies were initially reported, ¡®the most promising¡¯ candidates were often the most differentially expressed and could be obtained by a simple ranking of candidates based on fold change. As more data has become available, biologists have begun to look for ways [1-4] to use multiple data sou
%K Qualitative integration
%K High-throughput data
%K Heterogeneous data
%K Network
%K Network visualization
%K Candidate prioritization
%U http://www.biomedcentral.com/1752-0509/7/4