论文标题
通过矢量量化相互信息最大化自动癌的亚型
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
论文作者
论文摘要
癌症亚型对于理解肿瘤的性质和提供合适的治疗至关重要。但是，现有的标签方法在医学上是有争议的，并驱动了从教学信号中取消的过程。此外，癌症遗传表达谱是高维，稀缺且具有复杂依赖性的，从而对现有的亚型模型构成了严重的挑战，以输出明智的聚类。在这项研究中，我们提出了一种新型的聚类方法，用于以无监督的方式利用遗传表达谱并区分亚型。所提出的方法自适应地学习了从表达曲线的潜在表示模型输出的亚型输出的分类对应关系。通过最大化问题 - 输入表达概况和输出亚型之间的不可知论信息，我们的方法可以自动确定适当数量的亚型。通过实验，我们证明了我们提出的方法可以完善现有的有争议的标签，并且通过进一步的医学分析，这种改进被证明与癌症存活率有很高的相关性。
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven the process of subtyping away from teaching signals. Moreover, cancer genetic expression profiles are high-dimensional, scarce, and have complicated dependence, thereby posing a serious challenge to existing subtyping models for outputting sensible clustering. In this study, we propose a novel clustering method for exploiting genetic expression profiles and distinguishing subtypes in an unsupervised manner. The proposed method adaptively learns categorical correspondence from latent representations of expression profiles to the subtypes output by the model. By maximizing the problem -- agnostic mutual information between input expression profiles and output subtypes, our method can automatically decide a suitable number of subtypes. Through experiments, we demonstrate that our proposed method can refine existing controversial labels, and, by further medical analysis, this refinement is proven to have a high correlation with cancer survival rates.
