Kagan Dima, Jubran Juman, Yeger-Lotem Esti, Fire Michael
Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel.
Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf034.
Proteins act through physical interactions with other molecules to maintain organismal health. Protein-protein interaction (PPI) networks have proved to be a powerful framework for obtaining insight into protein functions, cellular organization, response to signals, and disease states. In multicellular organisms, protein content varies between tissues, influencing tissue morphology and function. Weighted PPI networks, reflecting the likelihood of interactions in specific tissues, offer insights into tissue-specific processes and disease mechanisms. We hypothesized that detecting anomalous nodes in these networks could reveal proteins with key tissue-specific functions.
Here, we introduce Weighted Graph Anomalous Node Detection (WGAND), a novel machine-learning algorithm to identify anomalous nodes in weighted graphs. WGAND estimates expected edge weights and uses deviations to generate anomaly detection features, which are then used to score network nodes. We applied WGAND to weighted PPI networks of 17 human tissues. High-ranking anomalous nodes were enriched for proteins associated with tissue-specific diseases and tissue-specific biological processes, such as neuron signaling in the brain and spermatogenesis in the testis. WGAND outperformed other methods in terms of area under the ROC curve and precision at K, highlighting its effectiveness in uncovering biologically meaningful anomalies.
Our findings demonstrate WGAND's potential as a powerful tool for detecting anomalous proteins with significant biological roles. By identifying proteins involved in critical tissue-specific processes and diseases, WGAND offers valuable insights for discovering novel biomarkers and therapeutic targets. Its versatile algorithm is suitable for any weighted graph and is broadly applicable across various fields. The WGAND algorithm is available as an open-source Python library at https://github.com/data4goodlab/wgand.
蛋白质通过与其他分子的物理相互作用来维持机体健康。蛋白质-蛋白质相互作用（PPI）网络已被证明是深入了解蛋白质功能、细胞组织、信号响应和疾病状态的有力框架。在多细胞生物中，蛋白质含量在不同组织间存在差异，影响着组织的形态和功能。加权PPI网络反映了特定组织中相互作用的可能性，有助于深入了解组织特异性过程和疾病机制。我们假设在这些网络中检测异常节点可以揭示具有关键组织特异性功能的蛋白质。
在此，我们介绍加权图异常节点检测（WGAND），一种用于识别加权图中异常节点的新型机器学习算法。WGAND估计预期边权重，并利用偏差生成异常检测特征，然后用于对网络节点进行评分。我们将WGAND应用于17种人体组织的加权PPI网络。排名靠前的异常节点富含与组织特异性疾病和组织特异性生物学过程相关的蛋白质，如大脑中的神经元信号传导和睾丸中的精子发生。在ROC曲线下面积和K值精度方面，WGAND优于其他方法，突出了其在发现具有生物学意义的异常方面的有效性。
我们的研究结果证明了WGAND作为检测具有重要生物学作用的异常蛋白质的强大工具的潜力。通过识别参与关键组织特异性过程和疾病的蛋白质，WGAND为发现新型生物标志物和治疗靶点提供了有价值的见解。其通用算法适用于任何加权图，广泛适用于各个领域。WGAND算法可在https://github.com/data4goodlab/wgand上作为开源Python库获取。
