Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data

• URL: http://arxiv.org/abs/2506.05542v1
• Date: Thu, 05 Jun 2025 19:44:38 GMT
• Title: Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data
• Authors: Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Aitor Alberdi Escudero, Edward Blake, David Cechak, Luke Cassar, Alessandro Balestrucci, Panagiotis Alexiou,
• Abstract summary: We introduce Agentomics-ML, a fully autonomous agent-based system designed to produce a classification model.<n>We show that Agentomics-ML outperforms existing state-of-the-art agent-based methods in both generalization and success rates.
• Score: 33.7054351451505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adoption of machine learning (ML) and deep learning methods has revolutionized molecular medicine by driving breakthroughs in genomics, transcriptomics, drug discovery, and biological systems modeling. The increasing quantity, multimodality, and heterogeneity of biological datasets demand automated methods that can produce generalizable predictive models. Recent developments in large language model-based agents have shown promise for automating end-to-end ML experimentation on structured benchmarks. However, when applied to heterogeneous computational biology datasets, these methods struggle with generalization and success rates. Here, we introduce Agentomics-ML, a fully autonomous agent-based system designed to produce a classification model and the necessary files for reproducible training and inference. Our method follows predefined steps of an ML experimentation process, repeatedly interacting with the file system through Bash to complete individual steps. Once an ML model is produced, training and validation metrics provide scalar feedback to a reflection step to identify issues such as overfitting. This step then creates verbal feedback for future iterations, suggesting adjustments to steps such as data representation, model architecture, and hyperparameter choices. We have evaluated Agentomics-ML on several established genomic and transcriptomic benchmark datasets and show that it outperforms existing state-of-the-art agent-based methods in both generalization and success rates. While state-of-the-art models built by domain experts still lead in absolute performance on the majority of the computational biology datasets used in this work, Agentomics-ML narrows the gap for fully autonomous systems and achieves state-of-the-art performance on one of the used benchmark datasets. The code is available at https://github.com/BioGeMT/Agentomics-ML.

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