Agentomics
Autonomous ML experimentation for biomedical data.
Published in Bioinformatics (2026) · DOI: 10.1093/bioinformatics/btag250
From data to trained model, fully automated.
Provide a labeled, folder-based dataset for classification or regression. Inputs can include tabular data, biological sequences, images, audio, or mixed files, with optional supplementary material. Agentomics evaluates multiple strategies and returns the best validated model, reusable scripts, and a report.
Core Design
Agentomics generates code from scratch for each dataset, selects among competing strategies, and runs the full experiment inside a sandboxed environment.
Secure by Default
Every run executes inside an isolated Docker container. Code is sandboxed, dependencies are installed on demand inside the container, and nothing touches your host environment.
Biomedical Foundation Models
Built-in support for ESM-2, HyenaDNA, NucleotideTransformer, RiNALMo, ChemBERTa and MolFormerXL. Any Hugging Face model can be added.
Verified Results
Every step must produce working code before the next begins. Agentomics always builds on validated, executable outputs so the metrics it reports reflect models that actually ran.
Reproducible Output
Every completed run stores its best model in a stable snapshot, alongside reusable training and inference scripts, model artifacts, a conda environment, and Markdown and PDF reports.
The 7-step pipeline
Runs iteratively.
Each cycle tries a new strategy until the best model is found.
Get Started
Requires Docker and Python 3.11+.
pip install agentomics
agentomics-download-dataset # Download an example dataset
export OPENROUTER_API_KEY=...
agentomics-runConfigure your provider: Set an API key, sign in with Codex, or use Ollama.
Add your dataset: Place your labeled data in datasets/<name>/.
Run Agentomics: Run agentomics-run and follow the prompts.
Use your outputs: Find your best model and reports in outputs/<agent_id>/.
Case Study: Human Enhancer Classification
Watch Agentomics train a DNA sequence classifier on the human enhancers dataset from scratch. The agent explores the dataset, selects a strategy, trains candidate models, scores them on a held-out validation split, and writes reproducible outputs without human intervention.
Benchmark Evaluation
Agentomics ranks first in every domain tested. Results show mean leaderboard score (higher is better).
Protein Engineering
6 datasets
Drug Discovery
9 datasets
Regulatory Genomics
5 datasets
Meet the Team
The people behind Agentomics.
Dr. Panagiotis Alexiou
ERA Chair, Bioinformatics
Dr. Panagiotis (Panos) Alexiou is the ERA Chair in Bioinformatics for Genomics at the University of Malta. His research focuses on the development of Machine Learning applications applied in Genomics.
Dr. Vlastimil Martinek
Research Scientist
Research scientist with experience in deep learning and computational biology research. Developed benchmarks and state-of-the-art deep learning models for genomics and transcriptomics.
Andrea Gariboldi
Research Scientist
Research scientist with hands-on experience in relational data modeling and SQL, and agentic LLM systems. Passionate about advancing capabilities in Generative AI and Machine Learning.
Dimosthenis Tzimotoudis
Research Scientist, PhD Candidate
Research Scientist and PhD candidate in Bioinformatics focusing on evolutionary biology. Develops small RNA deep learning models and genomic sequence mapping algorithms.
Mark Galea
Research Scientist
Research Scientist and team member with experience building deep learning models for audio event detection and face recognition systems.
David Čechák
PhD Candidate, Bioinformatics
Applies machine learning to uncover the rules governing miRNA and Ago2 binding to mRNA and subsequent gene regulation.
Edward Blake
Bioinformatician
Bioinformatician specializing in large-scale genomic analysis and machine learning model development for drug target identification in myocardial infarction.
Eng. Alessandro Balestrucci
Research Scientist, ICT Engineer
Expert in the full Knowledge Discovery process and AI technologies, pioneering novel methodologies from a research perspective to architect advanced AI solutions for cross-disciplinary research challenges.
Dr. Elissavet Zacharopoulou
Postdoctoral Researcher
Postdoctoral researcher with research interests in computational biology, machine learning, and data-driven analysis of molecular biology data.
Get in Touch
For collaboration inquiries, support, or to discuss using Agentomics in your research, reach out to us.