神奇：使用蛋白质 - 蛋白质相互作用网络预测合成致死和可行相互作用的多类分类器
MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network
影响因子:3.60000
分区:生物学2区 / 生化研究方法2区 数学与计算生物学2区
发表日期:2024 Aug
作者:
Anubha Dey, Suresh Mudunuri, Manjari Kiran
摘要
在癌症的靶向治疗方法中，通常研究了合成致死性（SL）和合成生存能力（SV）。在SL中，抑制任何一个基因都不会影响癌细胞的存活，而是抑制两种基因的生存，导致致命的表型。在SV中，抑制脆弱的基因会使癌细胞生病。抑制伴侣基因营救并促进细胞活力。已经采用了许多低通量和高通量实验方法来识别SLS和SVS，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。几乎所有的机器学习工具都是二进制分类器，仅涉及识别SL对。最重要的是，已知有限的属性，可以最能描述和区分SL与SV。我们通过算法学习开发了癌症中遗传相互作用的魔法（多级遗传相互作用），这是一种用于遗传相互作用预测的多类随机机器学习模型。源自物理蛋白质蛋白相互作用的蛋白质的网络性能用作对SL和SV进行分类的特征。该模型的训练数据集（CGIDB，Biogrid和SynlethDB）的准确性约为80％，并且在DEPMAP和其他实验得出的数据集上的性能很好。在所有网络属性中，最短路径，平均邻居2，平均中间，平均三角和粘附具有显着的歧视能力。 Magical是第一个识别合成致命和可行相互作用的歧视性特征的多类模型。与任何现有的二进制分类器相比，魔术可以以更好的准确性和精度来预测SL和SV相互作用。
Abstract
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.