IEEE/ACM Trans Comput Biol Bioinform. 2020 Sep-Oct;17(5):1682-1690. doi: 10.1109/TCBB.2019.2910515. Epub 2019 Apr 11.
In the age of personalized medicine, there is a great need to classify cancer (from the same organ site) into homogeneous subtypes. Recent technology advancements in genome-wide molecular profiling have made it possible to profiling multiple molecular datasets to characterize the genomic changes in various cancer types. How to take full advantage of the availability of these omics data? And how to integrate these molecular data with patient clinical data to do a more systematic subtyping of cancer are the focuses of the paper. We proposed a new method called Molecular and Clinical Networks Fusion (MCNF) to classify cancer into homogeneous subtypes. Our method has two highlights: one is that it can integrate both numerical and non-numerical data into the fused network; the next highlight is that it is unsupervised, which means it can automatically determine the optimal number of clusters.
在个性化医学时代，将癌症（来自同一器官部位）分类为同质亚型是非常有必要的。全基因组分子分析的最新技术进步使得对多个分子数据集进行分析以描述各种癌症类型的基因组变化成为可能。如何充分利用这些组学数据的可用性？以及如何将这些分子数据与患者临床数据相结合，对癌症进行更系统的亚型分类，是本文的重点。我们提出了一种称为分子和临床网络融合（MCNF）的新方法，将癌症分类为同质亚型。我们的方法有两个亮点：一是可以将数值和非数值数据集成到融合网络中；下一个亮点是它是无监督的，这意味着它可以自动确定最佳的聚类数量。
