Automated Author ProfileDhaka, Bhavya
Dhaka, Bhavya
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 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
HyperMapDB is a unified resource of predicted transcriptional responses to 19,036 genetic and chemical perturbations across 19 human cell lines. Predictions were generated using HyperMap, a meta-learning framework that transfers perturbation response knowledge from existing atlases to new biological contexts. The dataset contains predicted single-cell gene expression deltas for 2,500 genes.
Authors
- dhaka, bhavya ;
- gao, jiahao ;
- ideker, trey
HyperMapDB is a unified resource of predicted transcriptional responses to 19,036 genetic and chemical perturbations across 19 human cell lines. Predictions were generated using HyperMap, a meta-learning framework that transfers perturbation response knowledge from existing atlases to new biological contexts. The dataset contains predicted single-cell gene expression deltas for 2,500 genes.
Authors
- dhaka, bhavya ;
- gao, jiahao ;
- ideker, trey
Additional file 1: Table S1. TCGA samples with TP53 mutation status. Table S2. The annotated significant differentially accessibility peaks identified in breast and colon cancers. Table S3. Enrichment of CNA events in different distance bins. Tabel S4. Results of TF motif enrichment analysis (related to Fig. 2a-d). Table S5. Enrichment of non-B-form DNA structure in differentially accessible peaks. Table S6. Results from differential gene expression analysis between mutant and wild-type p53 tumours. Table S7. Summary gene list from the differential accessibility, differential expression, and overlap of both.
Authors
- Dhaka, Bhavya ;
- Sabarinathan, Radhakrishnan
Additional file 1: Table S1. TCGA samples with TP53 mutation status. Table S2. The annotated significant differentially accessibility peaks identified in breast and colon cancers. Table S3. Enrichment of CNA events in different distance bins. Tabel S4. Results of TF motif enrichment analysis (related to Fig. 2a-d). Table S5. Enrichment of non-B-form DNA structure in differentially accessible peaks. Table S6. Results from differential gene expression analysis between mutant and wild-type p53 tumours. Table S7. Summary gene list from the differential accessibility, differential expression, and overlap of both.
Authors
- Dhaka, Bhavya ;
- Sabarinathan, Radhakrishnan