Biologics repositioning: ml-SOM analysis raw results
<p><strong>Summary</strong></p> <p>This dataset contains raw result files for multiple-layer SOM (ml-SOM) repositioning analysis of infliximab and brodalumab. </p> <p><strong>Infliximab_results.zip</strong> archive contains the results of ml-SOM repositioning analysis of infliximab (T1) as a potential therapeutics for ulcerative colitis (D1), Crohn’s disease (D2), COPD (R1) and sarcoidosis (R2).</p> <p>Data is organized in the archive as follows:</p> <ol> <li>inflixmab_UC_CD_COPD_SARC021018_results+.RData - R data file that contains ml-SOM environment</li> <li>folder "inflixmab_UC_CD_COPD_SARC021018_results+ - Results" - comprises of various PDF and CSV files representing the a ml-SOM analysis results. It also contains the separate layer-level SOM analysis results for all datasets: <ul> <li>folder "1" - SOM analysis of infliximab dataset</li> <li>folder "2" - SOM analysis for ulcerative colitis and Crohn's disease dataset</li> <li>folder "3" - SOM analysis for COPD dataset</li> <li>folder "4" - SOM analysis for sarcoidosis dataset.</li> </ul> </li> </ol> <p><strong>Brodalumab_results.zip</strong> archive contains the results of ml-SOM repositioning analysis of brodalumab (T1) as a potential therapeutics for psoriasis (D1), Crohn’s disease (R1), and systemic juvenile idiopathic arthritis (R2).</p> <p>Data is organized in the archive as follows:</p> <ol> <li>brodalumab_CD_SJIA_301218_results+.RData - R data file that contains ml-SOM environment</li> <li>folder "brodalumab_CD_SJIA_301218_results+ - Results" - comprises of various PDF and CSV files representing the a ml-SOM analysis results. It also contains the separate layer-level SOM analysis results for all datasets: <ul> <li>folder "1" - SOM analysis of brodalumab and psoriasis dataset</li> <li>folder "2" - SOM analysis for Crohn's disease dataset</li> <li>folder "3" - SOM analysis for systemic juvenile idiopathic arthritis dataset</li> </ul> </li> </ol> <p>For detailed instructions on browsing the results and their interpretation please refer to the oposSOM package manual [1], as well as original publications [2-4]. </p> <p><strong>References</strong></p> <ol> <li>Henry Loeffler-Wirth, Hoang Thanh Le and Martin KalcheroposSOM.Comprehensive analysis of transcriptome data. DOI: <a href="https://doi.org/doi:10.18129/B9.bioc.oposSOM">10.18129/B9.bioc.oposSOM</a> </li> <li>Löffler-Wirth H, Kalcher M, Binder H. oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor.Bioinformatics. 2015 Oct 1;31(19):3225-7. DOI: 10.1093/bioinformatics/btv342. Epub 2015 Jun 10.</li> <li>Wirth H, von Bergen M, Binder H. Mining SOM expression portraits: feature selection and integrating concepts of molecular function. BioData Min. 2012 Oct 8;5(1):18. DOI: 10.1186/1756-0381-5-18.</li> <li>Wirth H, Löffler M, von Bergen M, Binder H. Expression cartography of human tissues using self organizing maps. BMC Bioinformatics. 2011 Jul 27;12:306. DOI: 10.1186/1471-2105-12-306.</li> </ol> <p> </p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0