# geoCancerPrognosticDatasetsRetriever GEO Cancer Prognostic Datasets Retriever is a bioinformatics tool for cancer prognostic dataset retrieval from the GEO website. ## Summary
Gene Expression Omnibus (GEO) Cancer Prognostic Datasets Retriever is a bioinformatics tool for cancer prognostic dataset retrieval from the GEO database. It requires a GeoDatasets input file listing all GSE dataset entries for a specific cancer (for example, bladder cancer), obtained as a download from the GEO database. This bioinformatics tool functions by applying two heuristic filters to examine individual GSE dataset entries listed in a GEO DataSets input file. The Prognostic Text filter flags for prognostic keywords (ex. “prognosis” or “survival”) used by clinical scientists and present in the title/abstract entries of a GSE dataset. If found, this tool retrieves those flagged datasets. Next, the second filter (Prognostic Signature filter) filters these datasets further by applying prognostic signature pattern matching (Perl regular expression signatures) to identify if the GSE dataset is a likely prognostic dataset.
## Installation geoCancerPrognosticDatasetsRetriever can be used on any Linux or macOS machines. To run the program, you need to have the following programs installed on your computer:Help information can be read by typing the following command:
```diff geoCancerPrognosticDatasetsRetriever -h ```This command will print the following instructions:
```diff Usage: geoCancerPrognosticDatasetsRetriever -h Mandatory arguments: CANCER_TYPE type of the cancer as query search term PLATFORM_CODES list of GPL platform codes Optional arguments: -h show help message and exit ``` ## Copyright and License Copyright 2021 by Abbas Alameer, Kuwait University This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 2 (GPLv2). ## ContactgeoCancerPrognosticDatasetsRetriever was developed by:
Abbas Alameer (Bioinformatics and Molecular Modelling Group, Kuwait University), in collaboration with Davide Chicco (University of Toronto)
For information, please contact Abbas Alameer at abbas.alameer(AT)ku.edu.kw