Machine Learning in Life Sciences Laboratory

Hi!! We investigate omic data using machine learning to unveil patterns in complex diseases, identify biomarkers, and predict outcomes and drug responses, contributing to the advancement of understanding and personalization in life sciences research.

Research Lines

Bioinformatics

Our team, composed of biologists and bioinformaticians, leverages computational tools to extract meaningful biological insights from a variety of genomic datasets. Our research spans from plant genomics to the study of infectious bacterial diseases, aiming to unravel the molecular mechanisms underlying these biological systems.

Biomedical Data Science

We apply data science techniques to analyze omic datasets, with a particular focus on identifying diagnostic and predictive biomarkers in complex diseases. Our goal is to support personalized medicine approaches by uncovering patterns and associations that inform clinical decision-making.

Omic Integration

Our research focuses on the integration of multi-omic data (genomics, transcriptomics, proteomics, etc.) to understand molecular interactions across different biological layers. By combining diverse datasets, we aim to enhance our understanding of disease mechanisms and improve strategies for diagnosis and treatment.