MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour type classification

Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource for understanding the genomic changes that occur during tumorigenesis. In this paper, we present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable Auto-Encoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions and a linear decoder, ensuring the interpretability of the active signatures in cancer genomes. We evaluated and compared MUSE-XAE with other available tools on synthetic and experimental cancer datasets and demonstrated that it achieves very accurate extraction capabilities while enhancing tumour-type classification. Our findings indicate that the use of Auto-Encoders is feasible and effective. This approach could facilitate further research in this area, with neural network-based models playing a critical role in advancing our understanding of cancer genomics