Deep learning with evolutionary and genomic profiles for identifying cancer subtypes

Chun-Yu Lin, Ruiming Li, Tatsuya Akutsu, Peiying Ruan, Simon See, Jinn-Moon Yang

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面147-150
頁數4
ISBN(電子)9781538662168
DOIs
出版狀態Published - 6 十二月 2018
事件18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018 - Taichung, Taiwan
持續時間: 29 十月 201831 十月 2018

出版系列

名字Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018

Conference

Conference18th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2018
國家Taiwan
城市Taichung
期間29/10/1831/10/18

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