Automated Author ProfileLv, Wen-Wen
Lv, Wen-Wen
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Table S1. Differentially expressed genes in high-PKM2 group. Table S2. Differentially expressed genes in low-PKM2 group. Table S3. Descriptive statistics of PKM2 and other clinical features grouped by hierarchical clustering. Table S4. Multivariate logistic regression between PKM2 and hierarchical clustering groups with adjusted covariates. Table S5. Details of gene set enrichment analysis in high-PKM2 vs. controls, low-PKM2 vs. controls and high-PKM2 vs. low-PKM2. Table S6. Correlation between PKM2 and metabolic genes in high-PKM2 group. Table S7. Correlation between PKM2 and metabolic genes in low-PKM2 group. Table S8. Survival analysis of up-regulated and PKM2-positively correlated metabolic genes. Table S9. Survival analysis of down-regulated and PKM2-negatively correlated metabolic genes. Table S10. Interaction of PKM2 and PKM2-uncorrelated metabolic genes on patientsâ overall survival. Table S11. Transcriptional regulation relationships of key metabolic genes. Table S12. Information of key metabolic genes and drugs. Table S13. Predicted compounds that affect risk metabolic genes by L1000CDS2 web tools. (XLSX 794 kb)
Authors
- Lv, Wen-Wen ;
- Liu, Dahai ;
- Liu, Xing-Cun ;
- Feng, Tie-Nan ;
- Li, Lei ;
- Qian, Bi-Yun ;
- Li, Wen-Xing
Table S1. Differentially expressed genes in high-PKM2 group. Table S2. Differentially expressed genes in low-PKM2 group. Table S3. Descriptive statistics of PKM2 and other clinical features grouped by hierarchical clustering. Table S4. Multivariate logistic regression between PKM2 and hierarchical clustering groups with adjusted covariates. Table S5. Details of gene set enrichment analysis in high-PKM2 vs. controls, low-PKM2 vs. controls and high-PKM2 vs. low-PKM2. Table S6. Correlation between PKM2 and metabolic genes in high-PKM2 group. Table S7. Correlation between PKM2 and metabolic genes in low-PKM2 group. Table S8. Survival analysis of up-regulated and PKM2-positively correlated metabolic genes. Table S9. Survival analysis of down-regulated and PKM2-negatively correlated metabolic genes. Table S10. Interaction of PKM2 and PKM2-uncorrelated metabolic genes on patientsâ overall survival. Table S11. Transcriptional regulation relationships of key metabolic genes. Table S12. Information of key metabolic genes and drugs. Table S13. Predicted compounds that affect risk metabolic genes by L1000CDS2 web tools. (XLSX 794 kb)
Authors
- Lv, Wen-Wen ;
- Liu, Dahai ;
- Liu, Xing-Cun ;
- Feng, Tie-Nan ;
- Li, Lei ;
- Qian, Bi-Yun ;
- Li, Wen-Xing