Our research combines Data Science and Computational and Systems Biology, integrating clinical, experimental, and molecular data to support biomedical research and clinical practice.

By combining machine learning, and biological modelling, we transform complex health and life science‑science data into scientific knowledge that supports research and health practices.

We work with heterogeneous sources such as electronic health records, medical images, and genomic, transcriptomic, proteomic, and cellular data, connecting molecular processes with patient level information in a unified analytical framework.

Health Data Integration – We provide robust infrastructure and platforms for  collecting, organizing, curating and integrating diverse healthcare and research data to ensure quality, interoperability, and secure access.

Predictive Tools for Healthcare – We collaborate with clinical teams to develop artificial intelligence models for early diagnosis, prognosis and optimization of patient treatment.

Computational Systems Biology – We transform complex biological and phenotypic datasets into scientific insights by applying state-of-the-art machine learning and statistical modeling.

• Gene Regulatory Networks and Disease Mechanisms: We decode the fundamental circuits governing cellular behavior. Through integrative multi-omics analysis, we interrogate these networks to uncover the mechanisms by which their dysregulation drives the transition from health to disease.
• Cellular Communication in Cancer Immunity: Leveraging high-resolution single-cell technologies, we investigate the dynamic interplay between the immune system and tumor cells. Our goal is to identify the biomarkers and cellular signatures that can halt disease progression, enabling more precise oncology care.

• Unravel pathophysiological insights from complex clinical and biological datasets.
• Biomarker discovery for disease, prognosis, and therapy response.
• Diagnosis and prognosis support using data-driven models.
• Linking molecular and clinical data for personalized medicine.
• Understanding disease mechanisms through computational modelling.
• Healthcare processes optimization supported by predictive analytics.

Meet the researchers working in Data Science and Computational Biology at iSTARS.

We collaborate with clinicians, researchers, institutions, and industry partners interested in applying data-driven‑driven and computational approaches to biomedical questions.

Whether you have clinical data, experimental datasets, or methodological challenges, iSTARS can support your research projects.