PR-NET: Leveraging Pathway Refined Network Structures for Prostate Cancer Patient Condition Prediction

arXiv:2403.05818v1 Announce Type: new
Abstract: Motivation: The diagnosis and monitoring of Castrate Resistant Prostate Cancer (CRPC) are crucial for cancer patients, but the current models (such as P-NET) have limitations in terms of parameter count, generalization, and cost. Results: To address the above issues, we develop a more accurate and efficient Prostate Cancer patient condition prediction model, named PR-NET. By compressing and optimizing the network structure of P-NET, the model complexity is reduced while maintaining high accuracy and interpretability. The …

abstract arxiv cancer cost count cs.lg current diagnosis limitations monitoring motivation network patient patients prediction q-bio.qm results terms type