Interpretable cancer stage classification using sparse bayesian neural networks

Cancer requires an in-depth exploration of its molecular characteristics. For targeted treatment strategies, distinguishing cancer stages is essential. This thesis has focused on the differentiation of early- and late-stage cancers using gene expression profiles. With the integration of computational techniques into medical research, machine learning models excel in this task, offering insights into biological mechanisms. To harness these insights, we proposed a novel approach, which is Bayesian Neural Networks (BNNs) with sparsity-inducing priors. The proposed sparse BNNs are designed to deliver high predictive performance in identifying cancer stages while maintaining a high level of interpretability. To evaluate our sparse BNN models, we benchmarked them against three machine learning algorithms across 15 different cancer cohorts. The results of our study revealed that our sparse BNN models achieve predictive performances comparable to traditional benchmark models. Additionally, we addressed the black-box issue of neural networks in medicine, which obscures which input features are crucial for predictions, a serious issue in decision-making with significant implications. To address this issue, our primary contribution has been the development of a novel BNN architecture that considerably enhances data interpretability. In our approach, we have integrated three types of sparsity inducing priors, namely, Laplace, Student's t, and Spike-and-Slab. Each prior has a mean of zero and low variance, promoting a reduction in connections and thus enabling a focused feature selection process. This methodology allows us to identify and concentrate on the most influential gene expressions. Our analysis revealed that sparse BNNs show a distinct preference for specific gene sets. In conclusion, the development of sparse BNNs offers a biologically informative and interpretative tool, enhancing the field of cancer research by shedding light on key gene pathways and significantly improving the process of cancer staging.