Ece Dilber Gamsiz Uzun

The paper develops a deep learning model called PLASMA to predict early recurrence of lung adenocarcinoma using clinical, mRNA expression, and mutation data, achieving high AUROC scores in validation sets.

LUNAR predicts glioma recurrence by integrating clinical, genomic, and mRNA expression data from grade II-IV glioma patients.

VGC is a VCF file visualization and analysis tool that provides variant data extraction, visualization, and integration with external databases for gene function and variant interpretation.

This study shows that VPA exposure alters chromatin environment, leading to changes in alternative splicing and gene expression in human cortical neurons, which may contribute to ASD pathogenesis.

The paper identifies a distinct molecular subtype of breast cancer associated with MMR deficiency, showing unique proteomic networks and variable clinical outcomes across subtypes.

LYRUS predicts the pathogenicity of missense variants using sequence, structure, and dynamics-based features, achieving higher accuracy than existing methods.

The paper uses iPSC-derived neurons from Christianson syndrome patients to show allele-specific responses to rescue strategies, highlighting the need for personalized therapeutic approaches.

VarStack retrieves somatic variant data from multiple databases to aid in cancer variant interpretation.