Péter Mináry

Probabilistic Modelling of Base Editing Efficiency and Bystander Outcome Composition

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: Probabilistic Modelling of Prime Editing Variant Correction Efficiency

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.

DeepEmbCas9: Cas9 coevolution and sgRNA structural information for CRISPR-Cas9 cleavage activity prediction

This paper introduces DeepEmbCas9 models to predict CRISPR-Cas9 cleavage activity across multiple variants using protein and RNA embeddings, showing improved performance and uncertainty quantification.

Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs

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

piCRISPR: Physically Informed Deep Learning Models for CRISPR/Cas9 Off-Target Cleavage Prediction

piCRISPR uses physically informed deep learning to predict CRISPR/Cas9 off-target cleavage, showing improved performance over existing methods by incorporating sequence context and chromatin accessibility.