Katarína Grešová

This study identifies a nucleolar role for Drosophila Argonaute 1, showing it associates with snoRNP machinery and regulates pre-rRNA levels and cell growth.

miRBind2 is a deep learning model that predicts miRNA binding and transcript repression from sequence, outperforming existing methods with fewer parameters.

SenCat catalogs senescence markers across 14 human cell types using transcriptomic and proteomic profiling, revealing shared metabolic pathways and enabling machine learning-based identification of senescent cells.

AlphaFind v2 enables fast, structure-based search across protein structures and domains, using embeddings and US-align for refinement and supporting organism and CATH-based filtering.

This study shows HSV-1 infection induces Aβ clustering and neurodegeneration pathways in iPSC-derived neurons, supporting the pathogen infection hypothesis for Alzheimer's disease.

This study identifies cell-type-specific RNA isoform changes in aging mouse brain using single-cell and long-read sequencing, revealing altered coding potential and senescence markers in immune cells.

The paper introduces miRBench, a benchmark dataset for microRNA binding site prediction that reduces miRNA frequency class bias, showing improved performance with a simple CNN model.

AlphaFind provides a web-based tool for fast structure-based retrieval of protein structures from AlphaFold DB using 3D features and machine learning.

The paper identifies AGO2 target sites using chimeric reads from CLASH and eCLIP experiments, revealing microRNA and non-microRNA drivers and developing predictive models and tools.

The paper introduces a collection of genomic sequence classification datasets and a baseline CNN model, distributed as a Python package to improve reproducibility in genomics machine learning.

This study evaluates Transformer models for genomic sequence classification, comparing pre-trained and from-scratch approaches on benchmark datasets.