Emad Amin, Hostallero David Earl
Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
Mila, Quebec AI Institute, Montreal, QC, Canada.
Methods Mol Biol. 2025;2932:273-289. doi: 10.1007/978-1-0716-4566-6_15.
Cancer precision medicine aims to identify the best course of treatment for an individual. To achieve this goal, two important questions include predicting the response of an individual to a treatment strategy and identifying molecular markers that determine the response. The rapid growth of large publicly available databases containing clinical and molecular characteristics of cancer-derived samples paired with their response to single or multiple drugs, has enabled the development of computational models to answer these questions. In recent years, various deep learning models have been proposed to predict the response to polytherapy and monotherapies. However, selecting among all available options or developing new models for a particular study requires careful considerations and best practices to avoid various pitfalls. In this chapter, and drawing from our own studies, we will discuss various important points for choosing, utilizing, and developing such deep learning tools.
癌症精准医学旨在为个体确定最佳治疗方案。为实现这一目标，两个重要问题包括预测个体对治疗策略的反应以及识别决定反应的分子标志物。包含癌症衍生样本的临床和分子特征及其对单药或多药反应的大型公共可用数据库的快速增长，使得能够开发计算模型来回答这些问题。近年来，已经提出了各种深度学习模型来预测对多药疗法和单药疗法的反应。然而，在所有可用选项中进行选择或为特定研究开发新模型需要仔细考虑和最佳实践，以避免各种陷阱。在本章中，我们将借鉴自己的研究，讨论选择、使用和开发此类深度学习工具的各种要点。
