Automated Author ProfileRoohani, Yusuf
Roohani, Yusuf
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: 1.5 (sum of 6 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf
Learning universal representations (embeddings) of single cell RNA-sequencing data is critical for drawing scientific conclusions from diverse omics datasets. Here we propose a foundation model that produces universal cell embeddings (UCE), which capture true biological variation despite experimental noise. This repository contains files needed to run the UCE model.
Authors
- Roohani, Yusuf