Automated Author ProfileMhlanga, Musa M.
Radboud University NijmegenRadboud University Nijmegen Medical Centre
Mhlanga, Musa M.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Additional file 1: Supplementary Tables. Table S1. Variant grouping strategies of 48 Interleukin-1 pathway related genes. Table S2. Molecular Inversion Probes (MIPs) covering all coding exons of 48 genes of the Interleukin-1 pathway. Table S3. Non-synonymous variant list. Table S4. Gene-level SKAT output. Table S5. Subpathway-level SKAT output. Table S6. Inflammatory-phenotype level SKAT output. Table S7. Validation SKAT output – permutations. Table S8. Synonymous variant list. Table S9. Validation SKAT output – synonymous. Table S10. Validation SKAT output – extreme outliers. Table S11. Common coding and non-coding IL38 set single SNP linear model parameters. Table S12. GWAS P-value versus SKAT adjP-value.
Authors
- van Deuren, Rosanne C. ;
- Arts, Peer ;
- Cavalli, Giulio ;
- Jaeger, Martin ;
- Steehouwer, Marloes ;
- van de Vorst, Maartje ;
- Gilissen, Christian ;
- Joosten, Leo A. B. ;
- Dinarello, Charles A. ;
- Mhlanga, Musa M. ;
- Kumar, Vinod ;
- Netea, Mihai G. ;
- van de Veerdonk, Frank L. ;
- Hoischen, Alexander
Additional file 1: Supplementary Tables. Table S1. Variant grouping strategies of 48 Interleukin-1 pathway related genes. Table S2. Molecular Inversion Probes (MIPs) covering all coding exons of 48 genes of the Interleukin-1 pathway. Table S3. Non-synonymous variant list. Table S4. Gene-level SKAT output. Table S5. Subpathway-level SKAT output. Table S6. Inflammatory-phenotype level SKAT output. Table S7. Validation SKAT output – permutations. Table S8. Synonymous variant list. Table S9. Validation SKAT output – synonymous. Table S10. Validation SKAT output – extreme outliers. Table S11. Common coding and non-coding IL38 set single SNP linear model parameters. Table S12. GWAS P-value versus SKAT adjP-value.
Authors
- van Deuren, Rosanne C. ;
- Arts, Peer ;
- Cavalli, Giulio ;
- Jaeger, Martin ;
- Steehouwer, Marloes ;
- van de Vorst, Maartje ;
- Gilissen, Christian ;
- Joosten, Leo A. B. ;
- Dinarello, Charles A. ;
- Mhlanga, Musa M. ;
- Kumar, Vinod ;
- Netea, Mihai G. ;
- van de Veerdonk, Frank L. ;
- Hoischen, Alexander