Bioinformatics and Human Electrophysiology Lab (BiHELab), Department of Informatics, Ionian University, Corfu, Greece.
Adv Exp Med Biol. 2023;1423:215-224. doi: 10.1007/978-3-031-31978-5_20.
Gene regulatory network (GRN) inference from gene expression data is a highly complex and challenging task in systems biology. Despite the challenges, GRNs have emerged, and for complex diseases such as neurodegenerative diseases, they have the potential to provide vital information and identify key regulators. However, every GRN method produced predicts results based on its assumptions, providing limited biological insights. For that reason, the current work focused on the development of an ensemble method from individual GRN methods to address this issue. Four state-of-the-art GRN algorithms were selected to form a consensus GRN from their common gene interactions. Each algorithm uses a different construction method, and for a more robust behavior, both static and dynamic methods were selected as well. The algorithms were applied to a scRNA-seq dataset from the CK-p25 mus musculus model during neurodegeneration. The top subnetworks were constructed from the consensus network, and potential key regulators were identified. The results also demonstrated the overlap between the algorithms for the current dataset and the necessity for an ensemble approach. This work aims to demonstrate the creation of an ensemble network and provide insights into whether a combination of different GRN methods can produce valuable results.
从基因表达数据推断基因调控网络（GRN）是系统生物学中一项高度复杂和具有挑战性的任务。尽管存在挑战，但 GRN 已经出现，对于复杂疾病（如神经退行性疾病），它们有可能提供重要信息并识别关键调节剂。然而，每种 GRN 方法的预测结果都是基于其假设得出的，提供的生物学见解有限。基于此，目前的工作侧重于开发一种基于个体 GRN 方法的集成方法来解决这个问题。选择了四种最先进的 GRN 算法，从它们共同的基因相互作用中形成一个共识 GRN。每个算法都使用不同的构建方法，为了更稳健的行为，还选择了静态和动态方法。将这些算法应用于 CK-p25 鼠模型神经退行过程中的 scRNA-seq 数据集。从共识网络构建了顶级子网，并确定了潜在的关键调节剂。结果还表明，当前数据集的算法之间存在重叠，需要采用集成方法。这项工作旨在展示一个集成网络的创建，并探讨是否可以结合不同的 GRN 方法来产生有价值的结果。
