Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201

Coevolutionary dynamics on scale-free networks

We investigate Bak-Sneppen coevolution models on scale-free networks with various degree exponents $\gamma$ including random networks. For $\gamma >3$, the critical fitness value $f_c$ approaches to a nonzero finite value in the limit $N \to \infty$, whereas $f_c$ approaches to zero as $2<\gamma \le 3$. These results are explained by showing analytically $f_c(N) \simeq A/_N$ on the networks with size $N$. The avalanche size distribution $P(s)$ shows the normal power-law behavior for $\gamma >3$. In contrast, $P(s)$ for $2 <\gamma \le 3$ has two power-law regimes. One is a short regime for small $s$ with a large exponent $\tau_1$ and the other is a long regime for large $s$ with a small exponent $\tau_2$ ($\tau_1 > \tau_2$). The origin of the two power-regimes is explained by the dynamics on an artificially-made star-linked network.Comment: 5 pages, 5 figure

The Moderating Effect Of Long-Term Orientation On The Relationship Between Interfirm Power Asymmetry And Interfirm Contracts: The Cases Of Korea And USA

The purpose of this study is to enhance our understanding of the effects of LTO culture on the contractual relationship between exchange parties under conditions in which varying levels of asymmetrical power structures exist. This study attempt to determine the validity of projecting conclusions originating from studies conducted in low LTO cultures such as U.S. and Western Europe to contractual relationships in the high LTO cultures of Asia. Therefore, investigations into the influence of LTO may be helpful in understanding contractual relationships formed in countries with differing levels of long-term orientation. Survey research was conducted to collect data from manufacturers, Structural Equation Modeling was used to purify measurement scales, and Multiple Regression was conducted to test the hypotheses. The findings show that LTO companies tend to prefer &ldquo;soft&rdquo; contracts, although they enjoy a power advantage over their suppliers; whereas low LTO partners with asymmetrical power advantages prefer &ldquo;hard&rdquo; contracts with explicitly detailed written requirements

FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries. FickleNet explores diverse combinations of locations on feature maps created by generic deep neural networks. It selects hidden units randomly and then uses them to obtain activation scores for image classification. FickleNet implicitly learns the coherence of each location in the feature maps, resulting in a localization map which identifies both discriminative and other parts of objects. The ensemble effects are obtained from a single network by selecting random hidden unit pairs, which means that a variety of localization maps are generated from a single image. Our approach does not require any additional training steps and only adds a simple layer to a standard convolutional neural network; nevertheless it outperforms recent comparable techniques on the Pascal VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201

Development of Classification Method of the Flattened Body Surface Figures for the Mass Customization of Men\u27s Formal Jacket

The purpose of this study is to develop a new body shape classification method using variables measured on the flattened figures of men\u27s body surface. It is designed to reflect the concrete characteristics of the men\u27s formal jacket patterns so that it becomes easy to be utilized for the mass customization of men\u27s formal jacket. 152 men\u27s body scan surfaces were flattened into development figures using automatic flattening software and 17 angles and 2 size differences were measured on the flattened figures. The measured sizes were put into factor analysis and 5 factors are extracted: \u27Width and protrusion of hip\u27, \u27Anteroposterior position of hip\u27, \u27Bending of shoulder\u27, \u27Protrusion of chest\u27 and \u27Sway back\u27. K-means clustering were conducted using extracted factor scores and 152 subjects were classified into 5 flattened figure types of \u27straight\u27, \u27sway back\u27, \u27bend forward\u27, \u27lean back-b\u27 and \u27lea back-I\u27. An estimation model for the flattened body surface figure types was developed using logistic regression analysis. The agreements between logistic regression model and k-means clustering were 90.8% on average. It became possible to anticipate the specific shapes of flattened body surface figures of the random subjects using the results of this study. It could be applied to the mass customization system and will make it easy to offer the jacket patterns tailored to the individual consumer\u27s body shapes