论文标题
翻译量子机智能，用于建模肿瘤学中的肿瘤动力学
Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology
论文作者
论文摘要
量化肿瘤负担的动力学揭示了有关治疗效果和耐药性的有用信息，这些信息在推进模型知识的药物开发（MIDD）方面起着至关重要的作用。量子机智能的出现通过量子力学的角度提供了对肿瘤动态的无与伦比的见解。本文介绍了一种名为$η-$ net的新型混合量子古典神经结构，该结构可以量化有关治疗效果的肿瘤负担的量子动力学。我们在两种主要用例中评估了我们提出的神经溶液，包括特定于同类的特定于患者和患者的建模。在硅数值中，对于量化的生物学问题，数值结果显示出$η-$净的高能力和表达性。此外，与代表学习的紧密联系 - 现代AI成功的基础，可以有效地转移从相关队列到目标患者的经验知识。最后，我们利用贝叶斯的优化来量化模型预测的认知不确定性，为$η-$ net铺平了道路，用于在临床用法的决策中可靠的AI。
Quantifying the dynamics of tumor burden reveals useful information about cancer evolution concerning treatment effects and drug resistance, which play a crucial role in advancing model-informed drug developments (MIDD) towards personalized medicine and precision oncology. The emergence of Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. This paper introduces a novel hybrid quantum-classical neural architecture named $η-$Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects. We evaluate our proposed neural solution on two major use cases, including cohort-specific and patient-specific modeling. In silico numerical results show a high capacity and expressivity of $η-$Net to the quantified biological problem. Moreover, the close connection to representation learning - the foundation for successes of modern AI, enables efficient transferability of empirical knowledge from relevant cohorts to targeted patients. Finally, we leverage Bayesian optimization to quantify the epistemic uncertainty of model predictions, paving the way for $η-$Net towards reliable AI in decision-making for clinical usages.
