Şenay Kafkas

This study uses whole-genome sequencing to analyze S. aureus isolates from Saudi Arabia, revealing higher genetic diversity and antimicrobial resistance in pilgrimage cities compared to non-pilgrimage cities.

NanoDesigner improves de novo nanobody design success rates by iteratively refining docking and CDR generation using an expectation maximization algorithm.

BORD improves biomedical concept recognition by using distant supervision and ontology hierarchy, outperforming existing methods for disease and phenotype identification.

DeepViral improves prediction of virus-host protein interactions by incorporating disease phenotypes and protein sequences.

DDIEM compiles therapeutic strategies for 299 inborn errors of metabolism, classifying treatments by mechanism of action using a new ontology.

The paper extracts pathogen-disease associations from literature using ontology and statistical methods, resulting in 3,420 associations integrated into a database.

PathoPhenoDB links human pathogens to disease phenotypes to support infectious disease research by integrating pathogen-disease associations and drug resistance information.

The paper predicts host-pathogen interactions by integrating phenotypic, functional, and taxonomic knowledge using an ontology-based machine learning method.

Vec2SPARQL integrates vector space representations with SPARQL queries to enable semantic similarity and classification within a single query for biomedical applications.

The paper describes a literature mining component of the Open Targets platform that identifies and ranks target-disease associations from PubMed and Open Access articles.