Çiğdem Sevim Bayrak

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set

LoGoFunc predicts pathogenic gain- and loss-of-function variants using ensemble learning with diverse biological features, outperforming existing tools.

A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity

The paper compiles 820 host genetic variants associated with COVID-19 susceptibility, develops a machine learning classifier to predict severity variants, and creates a web-based resource for exploring these variants.

Identification of Discriminative Gene-level and Protein-level Features Associated with Gain-of-Function and Loss-of-Function Mutations

The paper identifies gene- and protein-level features distinguishing gain-of-function and loss-of-function mutations using NLP and machine learning on a new database of pathogenic mutations.

Identifying novel high-impact rare disease-causing mutations, genes and pathways in exomes of Ashkenazi Jewish inflammatory bowel disease patients

The paper identifies 7 novel IBD-causing genes in Ashkenazi Jewish IBD patients using exome sequencing and analyzes their overlap with early-onset IBD and other diseases.