High-throughput screening yields vast amounts of biological data which can be highly challenging to interpret. In response, knowledge-driven approaches emerged as possible solutions to analyze large datasets by leveraging prior knowledge of biomolecular interactions represented in the form of biological networks. Nonetheless, given their size and complexity, their manual investigation quickly becomes impractical. Thus, computational approaches, such as diffusion algorithms, are often employed to interpret and contextualize the results of high-throughput experiments. Here, we present MultiPaths, a framework consisting of two independent Python packages for network analysis. While the first package, DiffuPy, comprises numerous commonly used diffusion algorithms applicable to any generic network, the second, DiffuPath, enables the application of these algorithms on multi-layer biological networks. To facilitate its usability, the framework includes a command line interface, reproducible examples, and documentation. To demonstrate the framework, we conducted several diffusion experiments on three independent multi-omics datasets over disparate networks generated from pathway databases, thus, highlighting the ability of multi-layer networks to integrate multiple modalities. Finally, the results of these experiments demonstrate how the generation of harmonized networks from disparate databases can improve predictive performance with respect to individual resources.

中文翻译：

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MultiPaths：使用扩散算法分析多层生物网络的 python 框架

高通量筛选会产生大量生物学数据，这些数据的解释极具挑战性。作为回应，知识驱动的方法作为可能的解决方案出现，通过利用以生物网络形式表示的生物分子相互作用的先验知识来分析大型数据集。尽管如此，考虑到它们的规模和复杂性，他们的人工调查很快变得不切实际。因此，计算方法（例如扩散算法）通常用于解释高通量实验的结果并将其背景化。在这里，我们介绍了 MultiPaths，这是一个由两个独立的 Python 包组成的框架，用于网络分析。第一个包 DiffuPy 包含许多适用于任何通用网络的常用扩散算法，第二个包 DiffuPath，使这些算法能够应用于多层生物网络。为了促进其可用性，该框架包括一个命令行界面、可重现的示例和文档。为了演示该框架，我们对三个独立的多点进行了几次扩散实验- 从通路数据库生成的不同网络上的组学数据集，因此突出了多层网络集成多种模式的能力。最后，这些实验的结果证明了如何从不同的数据库生成协调的网络可以提高对单个资源的预测性能。