Autoencoders reveal polyunsaturated fatty acids (PUFA)-Related metabolic signature linked to cancer risk
Background Metabolomics is a valuable tool for characterising biological mechanisms involved in cancer development, but produces complex datasets with intricate interdependencies. While linear dimension reduction techniques such as principal component analysis (PCA), have proven useful to summarise informative hidden patterns, biological evidence suggests metabolic relationships extend beyond linearity. Non-linear dimension reduction techniques, such as autoencoders…












