The accumulating availability of next-generation sequencing data offers an opportunity to pinpoint driver genes that are causally implicated in oncogenesis through computational models. Despite previous efforts made regarding this challenging problem, there is still room for improvement in the driver gene identification accuracy. In this paper, we propose a novel integrated approach called IntDriver for prioritizing driver genes. Based on a matrix factorization framework, IntDriver can effectively incorporate functional information from both the interaction network and Gene Ontology similarity, and detect driver genes mutated in different sets of patients at the same time. When evaluated through known benchmarking driver genes, the top ranked genes of our result show highly significant enrichment for the known genes. Meanwhile, IntDriver also detects some known driver genes that are not found by the other competing approaches. When measured by precision, recall and F1 score, the performances of our approach are comparable or increased in comparison to the competing approaches.

中文翻译：

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通过体细胞突变谱和基因功能信息的集成模型发现潜在的驱动基因

下一代测序数据的累积可用性为通过计算模型查明与肿瘤发生有因果关系的驱动基因提供了机会。尽管就此具有挑战性的问题做出了先前的努力，但驱动基因识别准确性仍存在改进的空间。在本文中，我们提出了一种称为IntDriver的新型集成方法，用于对驱动程序基因进行优先级排序。基于矩阵分解框架，IntDriver可以有效地整合来自交互网络和Gene Ontology相似性的功能信息，并同时检测在不同患者组中突变的驱动基因。当通过已知的基准驱动基因进行评估时，我们结果中排名最高的基因显示出对已知基因的高度丰富。同时，IntDriver还检测其他竞争方法找不到的某些已知驱动基因。通过精确度，召回率和F1分数进行衡量时，与竞争方法相比，我们的方法的性能相当或有所提高。