Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

文献类型： 外文期刊

第一作者： Gong, Jianting

作者： Gong, Jianting;Zhao, Yingwei;Heng, Xiantao;Chen, Yongbing;Sun, Pingping;He, Fei;Ren, Zilin;Ma, Zhiqiang;Ren, Zilin

作者机构：

关键词： pan-cancer; RAS pathway; multi-label classification; graph autoencoder

期刊名称：ELECTRONIC RESEARCH ARCHIVE （ 影响因子：0.8； 五年影响因子：0.8 ）

ISSN：

年卷期： 2023 年 31 卷 8 期

页码：

收录情况： SCI

摘要： The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of "hidden responders" which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS.

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