Designing integrative big data analytical frameworks to accelerate precision oncology

Abstract

This thesis outlines the design and development of CIRAFm (Cancer Integromics Research Application Framework). CIRAFm is a software framework for interactively analysing biomedical data within cancer research. It provides a no-code browser platform which allows translational researchers to conduct, record, share their analysis both remotely and in real-time. The flexibility and extensibility of the platform as a result of its modular architecture allows CIRAFm to easily support multiple technologies including deep-learning capabilities and various programming languages within its no-code analytical spaces. The traditional and AI-based analytical capabilities of CIRAFm were tested and validated through a set of use cases centring on a published CRC data cohort and a CRC tissue image dataset. Through the testing of multiple use cases CIRAFm has shown the potential to support a wide range of analytics from EDA to AI-based methods. It also addresses several key of interest within the current biomedical research landscape including result reproducibility and secure sharing, democratizing access to biomedical analytics, and providing greater flexibility to researchers and scientists with their analytical workflows.