Fotis E Psomopoulos

The paper introduces the Microbiology Galaxy Lab, a community-driven platform for reproducible and FAIR microbial data analysis, offering tools for various omics data types and use cases.

DOME Copilot automates the generation of transparent AI methodology reports for life science publications, matching or exceeding manual quality while reducing creation time.

The paper develops synth4bench, a synthetic data pipeline to benchmark tumor-only somatic variant callers, revealing inconsistencies in tool performance and highlighting the need for sequencing optimization and caller selection.

lncAPNet identifies lncRNA-mRNA interactions in cancer transcriptomic data using a graph-based deep learning framework, revealing lncRNA drivers in CLL and PRAD.

This study maps AI use in Greek Life Sciences through bibliometric analysis and a national survey, revealing strengths in methodology and barriers in data and resources.

APNet identifies differentially active drivers of severe COVID-19 using a sparse deep learning model combined with co-expression networks, outperforming existing methods in patient classification.

The paper evaluates nine computational tools for identifying SARS-CoV-2 lineages in wastewater sequencing data using simulated samples, finding most tools reliably detect lineages above 5% frequency.

The paper introduces Galaxy Training Network (GTN) as a framework for creating and sharing FAIR training materials for data analysis, with recent improvements to support educators and expand its use in various learning environments.

The paper develops a machine learning method to detect co-occurring mutation patterns in SARS-CoV-2 sequences, beyond strain-specific mutations, using clustering to create a dendrogram based on mutation features.