Rushank Goyal. Cancer Genetics, Volumes 268-269, Supplement 1, Page 27 (November 2022).
Abstract.
Cancer is a broad term for diseases characterized by uncontrollable and abnormal cell growth. It is the second-leading cause of death worldwide, with 9 million deaths each year; early cancer detection remains crucial for improving survival outcomes, especially in developing countries. In this research, a novel three-step framework based on quantum machine learning was developed using transcriptome data to identify key cancer biomarkers and combine them to create mathematical expressions that can predict the presence of cancer with high accuracy using the expression levels of five or fewer genes. Instead of relying on traditional black-box machine learning, the framework utilized a recently-developed technology called the quantum lattice to produce transparent and explainable models. The framework was trained and tested on ten datasets with data on ten different cancers. For each dataset, after initial filtering through XGBoost and statistical significance testing to identify differentially expressed genes, the quantum lattice was trained for 10 epochs using the Akaike Information Criterion as its loss function. The framework was trained and tested on ten datasets. Median accuracies, sensitivities and specificities of 91%, 92.5% and 87.5% respectively were obtained, with the top three accuracies being 100%, 100% and 99%. Overall, the models show better accuracies than previous research while using far fewer genes for predictions. In all, 38 biomarkers were identified, with 17 novel results, including 4 lncRNAs. The results obtained can be applied in practical settings for efficient early cancer detection and provide insights into associations between certain genes and types of cancer.
