治疗前的个性化癌症筛查为提高诊断准确性和治疗结果铺平了道路。大多数方法仅限于单一数据类型，并且不考虑特征之间的相互作用，而忽略了多模态和系统生物学可以提供的补充见解。在这个项目中，我们演示了使用图论通过单个网络进行数据集成，其中节点和边是特定于个体的。我们展示了早期、中期和晚期基于图形的 RNA-Seq 数据和组织病理学全幻灯片图像融合的结果，用于预测癌症亚型和严重程度。开发的方法如下：（1）我们创建单独的网络； (2)我们从这些图中计算个体之间的相似度； (3)我们在相似度矩阵上训练我们的模型； (4)我们使用宏F1分数来评估性能。管道元素的优缺点是在公开的现实数据集上进行评估的。我们发现基于图的方法可以比不研究交互的方法提高性能。此外，与基于单个数据的模型相比，合并多个数据源通常可以改善分类，尤其是通过中间融合。所提出的工作流程可以轻松适应其他疾病环境，以加速和增强个性化医疗保健。© 2023。作者。

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.© 2023. The Author(s).