Name
Title | ||
|---|---|---|
B.E.A.R. GeneInfo: a tool for identifying gene-related biomedical publications through user modifiable queries. |
Abstract | ||
|---|---|---|
Once specific genes are identified through high throughput genomics technologies there is a need to sort the final gene list to a manageable size for validation studies. The triaging and sorting of genes often relies on the use of supplemental information related to gene structure, metabolic pathways, and chromosomal location. Yet in disease states where the genes may not have identifiable structural elements, poorly defined metabolic pathways, or limited chromosomal data, flexible systems for obtaining additional data are necessary. In these situations having a tool for searching the biomedical literature using the list of identified genes while simultaneously defining additional search terms would be useful.We have built a tool, BEAR GeneInfo, that allows flexible searches based on the investigators knowledge of the biological process, thus allowing for data mining that is specific to the scientist's strengths and interests. This tool allows a user to upload a series of GenBank accession numbers, Unigene Ids, Locuslink Ids, or gene names. BEAR GeneInfo takes these IDs and identifies the associated gene names, and uses the lists of gene names to query PubMed. The investigator can add additional modifying search terms to the query. The subsequent output provides a list of publications, along with the associated reference hyperlinks, for reviewing the identified articles for relevance and interest. An example of the use of this tool in the study of human prostate cancer cells treated with Selenium is presented.This tool can be used to further define a list of genes that have been identified through genomic or genetic studies. Through the use of targeted searches with additional search terms the investigator can limit the list to genes that match their specific research interests or needs. The tool is freely available on the web at http://prostategenomics.org1, and the authors will provide scripts and database components if requested mdatta@mcw.edu |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1186/1471-2105-5-46 | BMC Bioinformatics |
Keywords | Field | DocType |
metabolic pathway,high throughput,biological process,genetics,bioinformatics,selenium,algorithms,data mining,microarrays,software design,gene structure | Gene,Computer science,sort,Genomics,Sorting,Bioinformatics,Genetics,DNA microarray | Journal |
Volume | Issue | ISSN |
5 | 1 | 1471-2105 |
Citations | PageRank | References |
11 | 0.82 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohui Zhou | 1 | 73 | 29.90 |
| Xinyu Wen | 2 | 11 | 0.82 |
| Hang Liu | 3 | 835 | 90.79 |
| Michael J Schlicht | 4 | 11 | 0.82 |
| Martin J. Hessner | 5 | 63 | 4.82 |
| Peter J Tonellato | 6 | 212 | 24.88 |
| Milton Datta | 7 | 18 | 2.15 |