Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance
作者：
Highlights：
• Breast cancer is the most frequent cancer in women.
• Autoencoder can be applied as preprocessing technique to extract meaningful information from DNA methylation.
• Importance of autencoded features regarding breast cancer recurrence is supported by experimental results.
• Literature and enrichment analysis confirms that the most important genes in the autoencoders are related to breast cancer.
摘要
•Breast cancer is the most frequent cancer in women.•Autoencoder can be applied as preprocessing technique to extract meaningful information from DNA methylation.•Importance of autencoded features regarding breast cancer recurrence is supported by experimental results.•Literature and enrichment analysis confirms that the most important genes in the autoencoders are related to breast cancer.
论文关键词：Autoencoder,Breast cancer,DNA methylation,Feature generation,Machine learning
论文评审过程：Received 9 September 2019, Revised 5 August 2020, Accepted 18 October 2020, Available online 22 October 2020, Version of Record 2 November 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101976
