A SHAP-based Interpretation for Leveraging MultiOmic Data and Pancreatic Cancer Prognostication using Deep Learning Techniques

Pancreatic cancer remains one of the most aggressive and lethal forms of cancer, with a five-year survival rate of only 10% despite advancements in medical technology. Traditional diagnostic and treatment approaches have proven inadequate, necessitating innovative strategies that combine data-driven insights with precision medicine. This study proposes the integration of Next-Generation Sequencing(NGS) along with Shapley Additive Explanations(SHAP), from which the data is obtained, and an integrated novel deep learning framework is integrated. For the pancreatic cancer prognosis, the analysis of complex biological data and identification of molecular components is done by the Convolution Neural networks(CNNs) and Recurrent neural networks(RNNs). To facilitate the development of personalized treatment plans and a comprehensive understanding the integration of different data like genomic, transcriptomic, proteomic, and epigenomic is used, which facilitates the development of personalized treatment plans. Our approach has improved the performance in predicting patient outcomes and has significantly improved the prognostication and treatment of pancreatic cancer.