Approved research

Deep Machine Learning Algorithm for Cancer Risk Prediction Using Genes, Environment, and Clinical Data

Lay summary

Our research project aims to make a significant contribution to understanding and predicting the development of cancer. By utilizing advanced techniques such as deep learning and integrating multiple data sources, including genetic, environmental, and clinical data, we expect to develop a powerful model capable of accurately assessing an individual's risk of developing cancer. This project will take approximately three years, during which we will carefully collect and preprocess the necessary data, develop sophisticated deep learning algorithms, and rigorously evaluate their performance.

The impact of this research on public health could be transformative. With improved risk prediction, healthcare providers can identify high-risk individuals earlier, allowing for targeted interventions and personalized prevention strategies. This has the potential to significantly reduce cancer incidence and mortality. In addition, by exploring the complex interactions between genetic, environmental and clinical factors, we aim to improve our understanding of cancer mechanisms and identify potential new biomarkers or targets for future therapeutic advances.

Overall, this comprehensive and interdisciplinary approach has the potential to revolutionize cancer prevention and treatment. It has the ability to improve public health by informing evidence-based interventions and policies, leading to better patient outcomes and quality of life. In addition, findings from this research project may lay the groundwork for future precision medicine approaches and personalized treatment plans, ultimately reducing the burden of cancer on individuals, families, and society as a whole.