Automated Author ProfileHan, Yong
Shanghai Jiao Tong UniversityShanghai Ninth People's Hospital
Han, Yong
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: 2.9 (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 1: Tables S1-4. Table S1. Inhibitory effects of palbciclib and dalpiciclib on tumor growth in HNMM-PDX models. Table S2. Baseline clinical characteristics and previous systemic therapies. Table S3. Baseline and lowest level of neutrophil and white blood cell counts for each patient. Table S4. Tumor burden and patient outcomes.
Authors
- Shi, Chaoji ;
- Ju, Houyu ;
- Zhou, Rong ;
- Xu, Shengming ;
- Wu, Yunteng ;
- Gu, Ziyue ;
- Wang, Ying ;
- Chen, Wanling ;
- Huang, Xinyi ;
- Han, Yong ;
- Sun, Shuyang ;
- Li, Chuwen ;
- Wang, Min ;
- Zhou, Guoyu ;
- Zhang, Zhiyuan ;
- Li, Jiang ;
- Ren, Guoxin
Additional file 1: Tables S1-4. Table S1. Inhibitory effects of palbciclib and dalpiciclib on tumor growth in HNMM-PDX models. Table S2. Baseline clinical characteristics and previous systemic therapies. Table S3. Baseline and lowest level of neutrophil and white blood cell counts for each patient. Table S4. Tumor burden and patient outcomes.
Authors
- Shi, Chaoji ;
- Ju, Houyu ;
- Zhou, Rong ;
- Xu, Shengming ;
- Wu, Yunteng ;
- Gu, Ziyue ;
- Wang, Ying ;
- Chen, Wanling ;
- Huang, Xinyi ;
- Han, Yong ;
- Sun, Shuyang ;
- Li, Chuwen ;
- Wang, Min ;
- Zhou, Guoyu ;
- Zhang, Zhiyuan ;
- Li, Jiang ;
- Ren, Guoxin
Additional file 2: Table S1. Information of 162 chemicals. Table S2. Epithelial and Mesenchymal Gene lists. Table S3. Primers in this study. Table S4. Primary antibodies. Table S5. Genetic annotation of cell lines in this study. Table S6. Average ExcessHSA scores of all compounds. Table S7. Genetic annotation of PDX models.
Authors
- Gu, Ziyue ;
- Shi, Chaoji ;
- Li, Jiayi ;
- Han, Yong ;
- Sun, Bao ;
- Zhang, Wuchang ;
- Wu, Jing ;
- Zhou, Guoyu ;
- Ye, Weimin ;
- Li, Jiang ;
- Zhang, Zhiyuan ;
- Zhou, Rong
Additional file 2: Table S1. Information of 162 chemicals. Table S2. Epithelial and Mesenchymal Gene lists. Table S3. Primers in this study. Table S4. Primary antibodies. Table S5. Genetic annotation of cell lines in this study. Table S6. Average ExcessHSA scores of all compounds. Table S7. Genetic annotation of PDX models.
Authors
- Gu, Ziyue ;
- Shi, Chaoji ;
- Li, Jiayi ;
- Han, Yong ;
- Sun, Bao ;
- Zhang, Wuchang ;
- Wu, Jing ;
- Zhou, Guoyu ;
- Ye, Weimin ;
- Li, Jiang ;
- Zhang, Zhiyuan ;
- Zhou, Rong