A5092832874- Genomics and Rare Diseases
- Genetic Associations and Epidemiology
- Computational Drug Discovery Methods
- Genomic variations and chromosomal abnormalities
- Statistical Methods in Clinical Trials
- Atherosclerosis and Cardiovascular Diseases
- Genomics and Phylogenetic Studies
- melanin and skin pigmentation
- Data Mining Algorithms and Applications
- Machine Learning and Data Classification
- Bioinformatics and Genomic Networks
- Cultural Differences and Values
- Mast cells and histamine
- RNA and protein synthesis mechanisms
Nationwide Children's Hospital
2023-2024
The Ohio State University
2023-2024
ITMO University
2023
Abstract The population of Russia consists more than 150 local ethnicities. ethnic diversity and geographic origins, which extend from eastern Europe to Asia, make the uniquely positioned investigate shared properties inherited disease risks between European Asian ancestries. We present analysis genetic phenotypic data a cohort 4,145 individuals collected in three metro areas western Russia. show presence multiple admixed ancestry clusters spanning primarily high identity-by-descent sharing...
Abstract Background Current single nucleotide variants (SNVs) pathogenicity prediction tools assess various properties of genetic and provide a likelihood causing disease. This information aids in variant prioritization – the process narrowing down list potential pathogenic variants, and, therefore, facilitating diagnostics. Assessing effectiveness SNV using ClinVar data is widely adopted practice. Our findings demonstrate that this conventional method tends to overstate performance...
Several observational studies have demonstrated a consistent pattern of decreased melanoma risk among patients with vitiligo. More recently, this finding has been supported by suggested genetic relationship between the two entities, certain variants significantly associated an increased melanoma, basal cell carcinoma, and squamous carcinoma but We compared 48 from recently published GWAS identified three variants-located in TYR, MC1R-DEF8, RALY-EIF2S2-ASIP-AHCY-ITCH loci- that correlated for...
Positive-unlabeled data consists of points with either positive or unknown labels. It is widespread in medical, genetic, and biological settings, creating a high demand for predictive positive-unlabeled models. The performance such models usually estimated using validation sets, assumed to be selected completely at random (SCAR) from known examples. For certain metrics, this assumption enables unbiased estimation when treating as positive/negative. However, the SCAR often adopted without...