Furkan Özden

The paper introduces crispAIBE, a probabilistic framework for predicting base editing efficiency and bystander allele composition, achieving high correlation and coverage on benchmark datasets.

crispAIPE quantifies uncertainty in prime editing pegRNA efficiency predictions using a transformer-based probabilistic model, achieving high correlation with experimental data and providing coverage guarantees.

The paper introduces RNAGEN, a GAN-based model to generate synthetic RNA sequences that bind to target proteins, validated with in vitro experiments for SOX2.

DAVP uses an agentic-LLM pipeline to prioritize genetic variants for diagnosis, achieving high recall in identifying causal variants across multiple datasets.

CoNVict is an agentic AI system that prioritizes copy number variants in rare disease diagnosis by ranking candidates through structured reasoning, outperforming existing methods in identifying causal variants.

UTRGAN generates 5' UTR sequences optimized for higher translation efficiency and gene expression, outperforming existing UTRs in multiple metrics.

This paper introduces crispAI, a neural network for predicting uncertainty in CRISPR off-target activity, and presents crispAI-aggregate for improved sgRNA prioritization.

ECOLE is a deep learning model for detecting copy number variants in whole exome sequencing data, achieving improved precision and recall over existing methods.

DECoNT improves CNV detection accuracy on WES data by learning from matched WES and WGS data, tripling duplication precision and doubling deletion precision.