Essential gene prediction models built so far are heavily reliant on sequence-based features and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we applied our approach for the prediction of essential genes to organisms from the STRING database and hosted the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2700+ bacteria predicted using features derived from STRING protein-protein functional association networks. Housing a total of 3.5M+ genes, NetGenes offers various features like essentiality scores, annotations and feature vectors for each gene. NetGenes is available at https://rbc-dsai-iitm.github.io/NetGenes/

中文翻译：

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NetGenes：使用相互作用网络中的特征预测的必需基因数据库

迄今为止建立的基本基因预测模型在很大程度上依赖于基于序列的特征，并且基于网络的特征的范围也很狭窄。我们小组以前的工作证明了使用基于网络的功能来高精度预测必需基因的重要性。在这里，我们将我们的方法用于从STRING数据库预测生物必需基因的方法，并将结果托管在一个独立的网站中。我们的数据库NetGenes包含2700多种细菌的基本基因预测，这些细菌使用来自STRING蛋白质-蛋白质功能关联网络的特征预测。NetGenes总共可容纳350万个基因，可为每个基因提供各种功能，如必需品得分，注释和特征向量。NetGenes可从https://rbc-dsai-iitm.github.io/NetGenes/获得