Deep Learning with Evolutionary and Genomic Profiles for Identifying Cancer Subtypes
Deep Learning with Evolutionary and Genomic Profiles for Identifying Cancer Subtypes
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DOI：
10.1109/bibe.2018.00035
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Chun-Yu Lin;Peiying Ruan;Ruiming Li;Jinn-Moon Yang;S. See;T. Akutsu
中科院分区：
文献类型：
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作者：
Chun-Yu Lin;Peiying Ruan;Ruiming Li;Jinn-Moon Yang;S. See;T. Akutsu
癌症亚型鉴定是精确诊断中未满足的需求。最近，进化保守已被指示包含可理解的签名在癌症中的功能意义。然而，进化保守性在区分癌症亚型中的重要性仍不清楚。在这里，我们鉴定了进化上保守的基因（即，核心基因），并观察到它们主要参与与细胞生长和代谢相关的途径。通过使用这些核心基因，我们将它们的进化和基因组图谱与深度学习相结合，开发了基于特征的策略（FES）和基于图像的策略（IMS）。与使用随机集的FES和使用PAM50分类器的策略相比，基于核心基因集的FES具有更高的乳腺癌亚型识别准确率。此外，IMS与数据增强产生更好的性能比其他策略。对8个TCGA癌症数据的综合分析表明，我们基于进化保守的模型提供了一种有效和有用的方法来识别癌症亚型，核心基因集提供了癌症亚型的可区分线索。
Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated containing understandable signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains unclear. Here, we identified the evolutionarily conserved genes (i.e., core gene) and observed that they are mainly involved in the pathways relevant to cell growth and metabolisms. By using these core genes, we integrated their evolutionary and genomic profiles with deep learning to develop a feature-based strategy (FES) and an image-based strategy (IMS). In comparison with FES using the random set and the strategy using the PAM50 classifier, core gene set-based FES has higher accuracy for identifying breast cancer subtypes. Moreover, the IMS with data augmentation yields better performance than the other strategies. Comprehensive analysis of eight TCGA cancer data demonstrates that our evolutionary conservation-based models provide a valid and helpful approach to identify cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.