Panagiotis Alexiou

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

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.

DiscrimAlign learns substitution matrices and gap penalties for sequence alignment to predict microRNA-target interactions, achieving performance comparable to neural networks while being more interpretable.

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.

The paper introduces RNAkinet, a deep learning model that uses nanopore sequencing to analyze RNA dynamics and cis-regulatory elements at single molecule resolution.

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.

This study investigates the role of the SAP domain in Ku-DNA interaction phases, showing it facilitates initial binding but not sliding, and finds that reduced loading efficiency does not impair NHEJ or telomere maintenance in Arabidopsis.

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 shows MOV10 restricts LINE1 retrotransposition in mice in a dosage-dependent manner, affecting reproductive fitness and transcriptome regulation.

ENNGene is a tool that simplifies deep learning model building for genomics data, achieving state-of-the-art results on the RBP24 dataset by incorporating evolutionary conservation and custom network tuning.

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

PENGUINN uses Convolutional Neural Networks to predict G-quadruplex forming sequences, outperforming existing methods.

MuStARD is a deep learning framework that identifies functional genomic elements across species by integrating sequence, structure, and conservation patterns.

GenOO is a Perl framework for HTS analysis that models biological entities as objects, enabling flexible and efficient development of custom tools.