標題: Deep learning with evolutionary and genomic profiles for identifying cancer subtypes
作者: Lin, Chun-Yu
Ruan, Peiying
Li, Ruiming
Yang, Jinn-Moon
See, Simon
Song, Jiangning
Akutsu, Tatsuya
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: Cancer subtype;evolutionary conservation;deep learning;convolutional neural network;cancer genomics;gene expression;copy number alteration
公開日期: 1-六月-2019
摘要: Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.
URI: http://dx.doi.org/10.1142/S0219720019400055
http://hdl.handle.net/11536/152144
ISSN: 0219-7200
DOI: 10.1142/S0219720019400055
期刊: JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
Volume: 17
Issue: 3
起始頁: 0
結束頁: 0
顯示於類別:期刊論文