Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy.
Translational and Precision Medicine Department, Sapienza University of Rome, Rome, Italy.
Bioinformatics. 2022 Sep 2;38(17):4145-4152. doi: 10.1093/bioinformatics/btac446.
Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.
In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines.
All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material.
Supplementary data are available at Bioinformatics online.
在过去的十年中，基于网络的方法已被证明在识别人类相互作用组中的疾病模块方面非常有用，这些方法通常可以深入了解关键机制，并为寻找治疗靶点提供指导。这一点尤为重要，因为对潜在基因候选物的实验研究是一项昂贵的任务，因此并不总是可行的选择。另一方面，除了相互作用组之外，还有许多生物信息源存在，设计有效的整合技术是一个重要的研究方向。
在这项工作中，我们引入了用于人类相互作用组中疾病基因优先级排序的生物随机游走（BRW）方法。所提出的框架利用集成框架内的多个生物源。我们对 BRW 的性能与成熟的基线进行了广泛的比较研究。
所有代码都公开可用，并可在 https://github.com/LeoM93/BiologicalRandomWalks 上下载。我们使用了公开可用的数据集，有关其检索和预处理的详细信息在补充材料中提供。
补充数据可在 Bioinformatics 在线获得。
