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

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

How Many U.S. Jobs Might Be Offshorable?

Using detailed information on the nature of work done in over 800 BLS occupational codes, this paper ranks those occupations according to how easy/hard it is to offshore the work— either physically or electronically. Using that ranking, I estimate that somewhere between 22% and 29% of all U.S. jobs are or will be potentially offshorable within a decade or two. (I make no estimate of how many jobs will actually be offshored.) Since my rankings are subjective, two alternatives are presented—one is entirely objective, the other is an independent subjective ranking. It is found that there is little or no correlation between an occupation’s “offshorability” and the skill level of its workers (as measured either by educational attainment or wages). However, it appears that, controlling for education, the most highly offshorable occupations were already paying significantly lower wages in 2004.

Human Age and Gender Classification using Convolutional Neural Networks

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, this research also focuses on the implementation of data augmentation techniques, some more novel than others, as a means of improving CNN performance. In terms of standard popular methods of augmentation, image mirroring and image rotation were applied. As well as these, a more novel approach to augmentation was applied to the area of age classification. This technique was completed using Faceapp, an AI image editor in the form of a mobile application. This application allows for the placement of ”filters” on images of human beings in order to alter their appearance. The results of the data augmented models were superior to that of the standard CNN models with gender classification improving by 2.6% while age classification improved by 7.1%. The results of this research establish the potential for further improvements through the inclusion of more augmentation techniques or through the use of more filter types provided in the Faceapp application

CWDM: A Case-based Diabetes Management Web System

Managing diabetes using intelligent techniques is a recent priority for healthcare information systems and the medical domain. Diabetes is one of the most widespread diseases around the world including Australia. Numerous intelligent systems supporting diabetes management (DM) have been widely deployed, yet how to effectively develop a DM system integrating intelligent techniques remains a big issue. Case-based reasoning (CBR), as an intelligent technique, has been applied in various fields including customer services, medical diagnosis, and clinical treatment. This paper proposes a case-based lifecycle for DM consisting of case-based symptoms, case-based diagnosis, case-based prognosis, case-based treatment, and case-based care. The lifecycle is integrated with a web-based system in which CBR functions as an intelligent intermediary. The approach proposed in this research might facilitate research and development of diabetes management, healthcare information systems and intelligent systems

Data-driven & Theory-driven Science : Artificial Realities and Applications to Savings Groups

Paper I and Paper II is not published yet. They are excluded from the dissertation until they will be published.The scientific process is neither unique nor nomic. Two processes of scientific inquiry are theory-driven and data-driven science. This dissertation analyzes savings groups using theory-driven and data-driven methods. Simulated realities-based on data-driven theory-are used to understand the emerging dynamics of savings groups. Savings groups are grassroots, community-based organizations composed of 15 to 30 members. These organizations-usually supported by international development agencies-have weekly meetings during a cycle of operations that typically lasts a year. In the groups, savings are kept in two funds: a fund for loans and a social welfare fund that covers life-cycle events. The findings of Papers A to D in this dissertation provide new large-sample evidence about savings groups, their dynamics, and the factors affecting their financial performance. In practice, the results of Paper A to D shed light on the best policies to promote sustainable development with informal finance in a cost-effective way. A theory-driven approach indicates that the social fund in savings groups stimulates loan allocation among risk-sharing members, while implicitly covering idiosyncratic risks (Paper A). A data-driven approach based on Bayesian data-mining reveals that the macroeconomic environment and the facilitation model of development agencies have a strong influence on the profit-generating capacity of savings groups (Paper B). Machine-learning methods further show that business training is not the most frequent program implemented by development agencies, but it is in fact the most powerful intervention to encourage profits, particularly when a development agency stops working with a group and leaves a community (Paper C). Finally, the simulation of a village with artificial agents indicates that the businesses of savings groups can have higher profits due to the consolidation of social capital and the competitive advantage created through a process of homophily (Paper D). Metatheoretically, the theory-driven and data-driven approaches of this dissertation-and the complementarity between these approaches-contribute to the epistemology of data-intensive science. The dissertation concludes that the gelstaltic and quasi-teleological explanations of the data-driven approach help to the formulation of theories through inductive and abductive reasoning.publishedVersio

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited