Automated Author ProfileSun, Yu
Jilin University
Sun, Yu
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: 7.4 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Additional file 2. Function enrichment analysis of top 4000 proteins. The function enrichment analysis is implemented by treating the entire set of human proteins as the background among the top 4000 proteins ranked by S-value, using DAVID against the GO and KEGG pathway.
Authors
- Du, Wei ;
- Sun, Yu ;
- Li, Gaoyang ;
- Cao, Huansheng ;
- Pang, Ran ;
- Li, Ying
Additional file 1. Thirty-Seven human saliva-secretory proteins that do not overlap with training set. These proteins are collected using the LC-MS/MS analyses reported in the literature and databases of SPD, LOCATE and UniProt. Then, the proteins in the training set are removed.
Authors
- Du, Wei ;
- Sun, Yu ;
- Li, Gaoyang ;
- Cao, Huansheng ;
- Pang, Ran ;
- Li, Ying
Additional file 2. Function enrichment analysis of top 4000 proteins. The function enrichment analysis is implemented by treating the entire set of human proteins as the background among the top 4000 proteins ranked by S-value, using DAVID against the GO and KEGG pathway.
Authors
- Du, Wei ;
- Sun, Yu ;
- Li, Gaoyang ;
- Cao, Huansheng ;
- Pang, Ran ;
- Li, Ying
Additional file 1. Thirty-Seven human saliva-secretory proteins that do not overlap with training set. These proteins are collected using the LC-MS/MS analyses reported in the literature and databases of SPD, LOCATE and UniProt. Then, the proteins in the training set are removed.
Authors
- Du, Wei ;
- Sun, Yu ;
- Li, Gaoyang ;
- Cao, Huansheng ;
- Pang, Ran ;
- Li, Ying