Identifying Cancer Subtypes Using Unsupervised Deep Learning

Glioblastoma multiforme (GBM) is the most fatal malignant type of brain tumor with a very poor prognosis with a median survival of around one year. Numerous studies have reported tumor subtypes that consider different characteristics on individual patients, which may play important roles in determining the survival rates in GBM. In this study, we present a pathway-based clustering method using Restricted Boltzmann Machine (RBM), called R-PathCluster, for identifying unknown subtypes with pathway markers of gene expressions. In order to assess the performance of R-PathCluster, we conducted experiments with several clustering methods such as k-means, hierarchical clustering, and RBM models with different input data. R-PathCluster showed the best performance in clustering longterm and short-term survivals, although its clustering score was not the highest among them in experiments. R-PathCluster provides a solution to interpret the model in biological sense, since it takes pathway markers that represent biological process of pathways. We discussed that our findings from R-PathCluster are supported by many biological literatures. Keywords. Glioblastoma multiforme, tumor subtypes, clustering, Restricted Boltzmann Machin

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain