37,397 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
The Control Fallacy: Why OA Out-Innovates the Alternative
This article examines the relationship between Open Access to the scholarly literature and innovation. It traces the ideas of "end to end" network principles in the Internet and the World Wide Web and applies them to the scholarly biomedical literature. And the article argues for the importance of relieving not just price barriers but permission barriers
A Survey of Natural Language Generation
This paper offers a comprehensive review of the research on Natural Language
Generation (NLG) over the past two decades, especially in relation to
data-to-text generation and text-to-text generation deep learning methods, as
well as new applications of NLG technology. This survey aims to (a) give the
latest synthesis of deep learning research on the NLG core tasks, as well as
the architectures adopted in the field; (b) detail meticulously and
comprehensively various NLG tasks and datasets, and draw attention to the
challenges in NLG evaluation, focusing on different evaluation methods and
their relationships; (c) highlight some future emphasis and relatively recent
research issues that arise due to the increasing synergy between NLG and other
artificial intelligence areas, such as computer vision, text and computational
creativity.Comment: Accepted by ACM Computing Survey (CSUR) 202
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