新治疗靶点的发现（定义为药物可以与之相互作用以产生治疗效果的蛋白质）通常代表药物发现的第一步也是最重要的一步。靶点发现的一个解决方案是靶点重新定位，这种策略依赖于对新疾病的已知靶点进行重新利用，从而带来新的治疗方法、更少的副作用和潜在的药物协同作用。生物网络已成为整合异构数据并促进生物或治疗特性预测的强大工具。因此，它们被广泛用于通过表征潜在候选者来预测新的治疗靶点，通常基于它们在蛋白质-蛋白质相互作用（PPI）网络中的相互作用，以及它们与疾病相关基因的接近程度。然而，过度依赖 PPI 网络以及潜在目标必然接近已知基因的假设可能会引入偏差，从而限制这些方法的有效性。本研究通过两种方式解决这些局限性。首先，通过利用多层网络，其中包含额外的信息，例如基因调控、代谢物相互作用、代谢途径和多种疾病特征，例如差异表达基因、突变基因、拷贝数改变和结构变异。其次，通过使用多种方法从网络中提取相关特征，包括接近疾病相关基因，但也包括基于传播的方法、拓扑度量和模块检测算法等无偏方法。以前列腺癌为案例研究，确定并利用最佳特征来训练机器学习算法，以预测 5 个有前景的前列腺癌新治疗靶点：IGF2R、C5AR、RAB7、SETD2 和 NPBWR1。© 2024 作者。

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.© 2024 The Authors.