Automated Author ProfileJunhua Gu
Junhua Gu
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: 1.2 (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 Table S1. The feature importance scores of the top protein features of the model. Supplementary Table S2. The feature importance scores of the top aptamer features of the model. Supplementary Table S3. The information of aptamers. Supplementary Table S4. The information of proteins. Supplementary Table S5. The datasets of protein-aptamer interactions prediction. Supplementary Table S6. The performance comparison before and after removing redundancy of the dataset. Supplementary Table S7. The datasets for predicting aptamers. Supplementary Table S8. Performance comparison between using adaboost alone and using adaboost and random forest in combination. Supplementary Table S9. Comparison of prediction performance of different machine learning algorithms for predicting protein-aptamer interactions.
Authors
- Li, Jianwei ;
- Xiaoyu Ma ;
- Xichuan Li ;
- Junhua Gu
Additional file 1: Supplementary Table S1. The feature importance scores of the top protein features of the model. Supplementary Table S2. The feature importance scores of the top aptamer features of the model. Supplementary Table S3. The information of aptamers. Supplementary Table S4. The information of proteins. Supplementary Table S5. The datasets of protein-aptamer interactions prediction. Supplementary Table S6. The performance comparison before and after removing redundancy of the dataset. Supplementary Table S7. The datasets for predicting aptamers. Supplementary Table S8. Performance comparison between using adaboost alone and using adaboost and random forest in combination. Supplementary Table S9. Comparison of prediction performance of different machine learning algorithms for predicting protein-aptamer interactions.
Authors
- Li, Jianwei ;
- Xiaoyu Ma ;
- Xichuan Li ;
- Junhua Gu