计算机科学
聚类分析
人工智能
降维
深度学习
机器学习
高维数据聚类
软件
数据挖掘
模式识别(心理学)
程序设计语言
作者
Runpu Chen,Le Yang,Steve Goodison,Yijun Sun
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2019-10-11
卷期号:36 (5): 1476-1483
被引量:90
标识
DOI:10.1093/bioinformatics/btz769
摘要
Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html.Supplementary data are available at Bioinformatics online.
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