合成致死率（SL）和合成活力（SV）是癌症靶向治疗方法中经常研究的遗传相互作用。在 SL 中，抑制任何一个基因都不会影响癌细胞的存活，但抑制这两个基因会导致致命的表型。在 SV 中，抑制脆弱基因会使癌细胞生病；抑制伴侣基因可挽救并促进细胞活力。许多低通量和高通量实验方法已被用来识别 SL 和 SV，但它们既耗时又昂贵。 SL 预测的计算工具涉及统计和机器学习方法。几乎所有机器学习工具都是二元分类器，并且仅涉及识别 SL 对。最重要的是，已知能够最好地描述和区分 SL 和 SV 的属性有限。我们开发了 MAGICAL（通过算法学习实现癌症遗传相互作用的多类方法），这是一种基于多类随机森林的机器学习模型，用于遗传相互作用预测。源自物理蛋白质-蛋白质相互作用的蛋白质的网络特性被用作对 SL 和 SV 进行分类的特征。该模型的训练数据集（CGIdb、BioGRID 和 SynLethDB）准确率约为 80%，并且在 DepMap 和其他实验得出的报告数据集上表现良好。在所有网络属性中，最短路径、平均neighbor2、平均介数、平均三角形和粘附力具有显着的判别力。 MAGICAL 是第一个识别合成致命和可行相互作用的歧视特征的多类模型。 MAGICAL 可以比任何现有的二元分类器更准确地预测 SL 和 SV 相互作用。版权所有：© 2024 Dey 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.Copyright: © 2024 Dey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.