文献类型：期刊文献

英文题名：CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

作者：Liu, Yawen[1,2]; Chen, Yiwen[3]; Nie, Shanling[4]; Yang, Hai[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [3] National University of Singapore, Center for Continuing and Lifelong Education, Singapore; [4] University of Sydney, Faculty of Engineering, Australia

年份：2024

起止页码：4966

外文期刊名：Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

收录：EI(收录号：20250717853636)

语种：英文

外文关键词：Contrastive Learning - Genome

摘要：With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. ? 2024 IEEE.