Healthcare and biotechnology generated unprecedented volumes of data, ranging from patients’ digital records (e.g., biomedical informatics) to clinical trials and medical researches. On the other hand, multiple research findings suggest that the outcomes in healthcare and biotechnology are the result of many factors’/entities'/variables’ interactions. Therefore, releasing the huge potential of these many islands of Big Data requires machine-learning algorithms that are capable of linking them and extracting such complex inter-factor relationships at the largest scales.
ConnectomeX is transforming the use of such islands of Big Data for solving the most complex problems in healthcare and biotechnology. Founded in 2013 after a decade of research at The University of Oxford (and more recently The Human Connectome Project), ConnectomeX is pioneering a new approach in automatic discovery of insights from Big Data and operationalising it for accurate decision-making. The implicit and explicit linkages of multiple databases, and distilling the resulting data with ensembles of connectomic machine learning algorithms, when combined with deep domain expertise, result in hubs of replicable predictive patterns/insights. These patterns sit behind ConnectomeX’s ecosystem, including intelligent query tools, recommender systems and user interfaces, to enable clinicians, domain experts, data scientists, and business professionals to achieve value from their data (even without the need to write code, queries or even ask questions) and improve their decision making.