Dario Strbenac

TRACE provides a framework for creating traceable biomedical resources, demonstrated with a single-cell glaucoma dataset, enabling reproducible analyses and resource reuse.

BenchHub provides a modular ecosystem for community-focused benchmarking in computational biology by linking datasets, metrics, and ground truth to improve reproducibility and sustainability.

The paper uses machine learning to analyze neuronal morphology in Parkinson's disease iPSC-derived neurons, identifying genotype-specific mitochondrial and lysosomal abnormalities.

This study identifies CCBE1 as a gene associated with mitochondrial dysfunction in atherosclerosis using transcriptomic analysis of endothelial cells from CAD patients.

This study compares dopamine and cortical neurons with PD mutations to identify cellular dysfunctions linked to different pathological phenotypes.

The paper develops a framework to construct multi-platform precision diagnostic pathways automatically, optimizing for accuracy and cost-effectiveness in clinical settings.

The paper introduces a method to handle omics data variation across platforms and times, showing its use in three disease risk models for clinical application.

The paper identifies diet-responsive genes in Drosophila by analyzing genetic variation across diets and confirms their role in nutrient storage and utilization.

This study evaluates stably expressed genes (SEGs) in single cells using a computational framework and finds they are more stable than traditional housekeeping genes across species.

DCARS identifies gene pairs with significant changes in correlation across survival stages, outperforming existing methods in cancer datasets.