List of network properties used as features.

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.

合成致死（Synthetic lethality, SL）与合成存活（Synthetic viability, SV）是癌症靶向治疗领域中备受关注的常见遗传互作模式。就SL而言，单独抑制任一基因均不会对癌细胞存活造成影响，但同时抑制二者则会引发致死表型。就SV而言，抑制易感基因会使癌细胞功能受损；而抑制其伴侣基因则可挽救癌细胞并提升细胞存活能力。目前已有多种低通量与高通量实验方法可用于鉴定SL与SV互作，但此类方法不仅耗时耗力，且成本高昂。用于SL预测的计算工具涵盖统计学与机器学习方法，但现有机器学习工具几乎均为二分类器，且仅能识别SL互作对。更为关键的是，目前能够精准描述并区分SL与SV的特征属性仍较为匮乏。本研究开发了MAGICAL（Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning，癌症遗传互作算法学习多类别分类方法）——一种基于多类别随机森林的遗传互作预测机器学习模型。本研究采用源自物理蛋白质互作数据的蛋白质网络属性作为特征，以实现SL与SV的分类任务。该模型在训练数据集（CGIdb、BioGRID及SynLethDB）上的准确率约为80%，且在DepMap及其他实验验证公开数据集上亦表现优异。在所有网络属性中，最短路径、平均邻居数2（average neighbor2）、平均介数、平均三角形数及粘附性均具备显著的区分能力。MAGICAL是首个可识别合成致死与合成存活互作区分特征的多类别分类模型。相较于现有所有二分类器，MAGICAL在SL与SV互作预测任务中具备更优异的准确率与精确率。