Automated Author ProfileZolotovskaya, Marianna
Zolotovskaya, Marianna
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: 3.6 (sum of 6 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Endocrine pathologies including disorders such as diabetes and dysfunctions of endocrine glands, are frequently associated with genetic predisposition. This study investigated the association of endocrine diseases with genetic variants, copy number variations (CNVs), and mutational load of molecular pathways in 4302 patients with 409 ICD-10 diagnoses who underwent DNA testing using next-generation sequencing at the National Medical Research Center for Endocrinology (Moscow) from November 2017 till January 2024. We analyzed rare protein-altering genetic variants using three control cohorts (gnomAD3, RUSeq healthy, experimental). We identified 143 associated variants for diabetes mellitus and 188 genetic variants across other 18 different ICD-10 groups of diagnoses, including 25% and 30% of previously undescribed variants, respectively. In addition, we investigated the aggregation of genetic variants across individual genes and their functional ensembles (molecular pathways) and identified 105 and 101 associations with ICD-10 diagnoses, respectively. In addition, we identified 35 pathogenic and 91 likely pathogenic CNVs in 925 patients with whole exome sequencing profiles. Among them, 9 and 44 CNVs, respectively, were not previously described. Totally, we found statistically significant associations between CNVs and endocrine pathologies for 168 genes. These results expand our understanding of endocrine disease mechanisms and may indicate new potential therapeutic targets.
Authors
- Zolotovskaya, Marianna
Endocrine pathologies including disorders such as diabetes and dysfunctions of endocrine glands, are frequently associated with genetic predisposition. This study investigated the association of endocrine diseases with genetic variants, copy number variations (CNVs), and mutational load of molecular pathways in 4302 patients with 409 ICD-10 diagnoses who underwent DNA testing using next-generation sequencing at the National Medical Research Center for Endocrinology (Moscow) from November 2017 till January 2024. We analyzed rare protein-altering genetic variants using three control cohorts (gnomAD3, RUSeq healthy, experimental). We identified 143 associated variants for diabetes mellitus and 188 genetic variants across other 18 different ICD-10 groups of diagnoses, including 25% and 30% of previously undescribed variants, respectively. In addition, we investigated the aggregation of genetic variants across individual genes and their functional ensembles (molecular pathways) and identified 105 and 101 associations with ICD-10 diagnoses, respectively. In addition, we identified 35 pathogenic and 91 likely pathogenic CNVs in 925 patients with whole exome sequencing profiles. Among them, 9 and 44 CNVs, respectively, were not previously described. Totally, we found statistically significant associations between CNVs and endocrine pathologies for 168 genes. These results expand our understanding of endocrine disease mechanisms and may indicate new potential therapeutic targets.
Authors
- Zolotovskaya, Marianna
Supplementary table S11 in "csv" format for article "Mutation enrichment and transcriptomic activation signatures of 419 molecular pathways in cancer" in journal "Cancers"
Authors
- Zolotovskaya, Marianna
Supplementary table S11 in "csv" format for article "Mutation enrichment and transcriptomic activation signatures of 419 molecular pathways in cancer" in journal "Cancers"
Authors
- Zolotovskaya, Marianna
Supplementary table S11 in "xlsx" format for article "Mutation enrichment and transcriptomic activation signatures of 419 molecular pathways in cancer" in journal "Cancers"
Authors
- Zolotovskaya, Marianna
Supplementary table S11 in "xlsx" format for article "Mutation enrichment and transcriptomic activation signatures of 419 molecular pathways in cancer" in journal "Cancers"
Authors
- Zolotovskaya, Marianna