Predictive Systems in Biomedicine Lab

The Predictive Systems in Biomedicine (PRISM) Lab, led by Dr Fayyaz Minhas at the University of Warwick, develops machine learning and artificial intelligence methods to understand complex biological and clinical systems. Our vision is to build algorithms behind the cure by building methods and applications that transform large and diverse biomedical datasets into insights that advance biological discovery and improve patient care.

The PRISM Lab is based in the Department of Computer Science and works closely with the Tissue Image Analytics Centre. We collaborate with clinicians, biologists, and industry partners worldwide to translate advances in AI into biomedical discovery, clinical decision support, and therapeutic innovation.

Research Programme

Our research develops machine learning frameworks that transform complex biomedical data into actionable scientific and clinical insight. We work across computational pathology, oncology, laboratory medicine, bioinformatics, and biomedical signal analysis, spanning scales from proteins and small molecules to cells, tissues, and patients, with a focus on diagnostic, prognostic, and predictive biomarker discovery.

We develop both core methodological advances and their application to real-world biomedical and clinical problems. This work is organised into two complementary themes:

Core Methodological Approaches for Biomedicine

• Learn: Multimodal and Spatial Representation Learning and Predictive Modelling for Biomedicine
  Developing machine learning models for predictive modelling and inference across heterogeneous data types, including images, spatial omics, genomic data, clinical records, and biomedical signals. This includes learning unified multimodal representations that capture biological structure and variability across scales. Specifically, we focus on building geometric and temporal models to capture spatial organisation, cell–cell interactions, tissue architecture, and disease progression over time, enabling the discovery of spatial and dynamic biomarkers.

• Explain: Interpretable, Causal, and Robust AI in Biomedicine
  Designing models that are transparent, reliable, and clinically meaningful, using causal representation learning, confounder-aware modelling, and principled interpretability to ensure robust and generalisable predictions across datasets, institutions, and populations.

• Deploy: Translational AI and Agentic Systems
  Developing AI systems that integrate modelling, reasoning, and decision-making into end-to-end workflows, enabling reproducible analysis pipelines and deployment in scientific and clinical settings.

Biomedical Applications and Translation

Applying these methods to high-impact problems across:

• Computational Pathology and Oncology
  Biomarker discovery, risk stratification, and prediction of disease progression and treatment response from histology and multimodal data (e.g., for colorectal cancer, analysis of fertility, etc.)

• Bioinformatics and Computational Biology (Proteins and Small Molecules)
  Modelling molecular/protein structure, interactions, and function for biomarker discovery and therapeutic insight esp. for antimicrobial discovery and protein-protein and protein-small molecule interactions.

• Laboratory Medicine
  Predictive modelling from laboratory test data and clinical records for diagnosis, monitoring, and risk prediction.

• Biomedical Signal Analysis
  Analysis of physiological and acoustic signals, including cardiac electrophysiology (e.g., ventricular tachycardia) and respiratory sounds (e.g., cough), for detection, characterisation, and clinical decision support.

For recent research talks, please see this page.

Research Funding and Outputs

For publications and patents, please visit this link.

For Research Funding details please see this link.

Prospective Students

If you are aprospective PhD, Masters or undergraduate studentinterested in working in the PRISM lab, please click here for details.Link opens in a new window

Email Contact:fayyaz.minhas@warwick.ac.uk

Current Members

Lead Investigator

• Dr. Fayyaz Minhas

Post-Doctoral Fellows

• Dr. Mark Eastwood
• Dr. Ruqayya Awan
• Dr. Haoran Ni (via CAMaCS)
• Dr. Julio Hurtado (via CAMaCS)

Current PhD Students

• Piotr Keller
• George Wright
• Ethar AlZaid
• Anja Estermann
• Agrima Agarwal (Co-Supervision with Prof. Emma McPherson)
• Joseph Mayer (Co-Supervision with Prof. Tarvinder Dhanjal)

Past PhD Students

• Dr. Muhammad Dawood
• Dr. Srijay Deshpande
• Dr. John Pocock
• Dr. Rawan AlBusayli
• Dr. Adiba Yaseen
• Dr. Amina Asif
• Dr. Wajid Abbasi
• Dr. Sadaf Gull
• Dr. Hira Kamal

Other Alumni

• Ammar Khairi (Research Assistant)