613 research outputs found
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 including random networks. For ,
the critical fitness value approaches to a nonzero finite value in the
limit , whereas approaches to zero as .
These results are explained by showing analytically on the networks with size . The avalanche size distribution
shows the normal power-law behavior for . In contrast,
for has two power-law regimes. One is a short regime for
small with a large exponent and the other is a long regime for
large with a small exponent (). 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 “soft” contracts, although they enjoy a power advantage over their suppliers; whereas low LTO partners with asymmetrical power advantages prefer “hard” 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
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