Dimosthenis Tzimotoudis

The paper introduces pre-miRBench, a multispecies benchmark for pre-miRNA prediction, showing its model outperforms existing predictors in cross-species and cross-family tests.

Agentomics is an autonomous system that generates ML models for biomedical tasks, outperforming existing agentic systems and human-engineered models on 11/20 datasets.

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

ParaDISM improves read placement and variant calling accuracy in highly homologous genomic regions by reducing misalignment artifacts.

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.