초고성능 컴퓨팅 기반의 건강한 고령사회 대응 빅데이터 기술 개발

비매

병원경영 진단 프로그램 개발에 관한 연구 : ASP기반 전자챠트를 활용한

학위논문(석사)--서울대학교 보건대학원 :보건학과 보건학 전공,2001.Maste

Development of Network Analysis and Visualization System for KEGG Pathways

Big data refers to informationalization technology for extracting valuable information through the use and analysis of large-scale data and, based on that data, deriving plans for response or predicting changes. With the development of software and device for next generation sequencing, a vast amount of bioinformatics data is generated recently. Also, bioinformatics data based big-data technology is rising rapidly as a core technology by the bioinformatician, biologist and big-data scientist. KEGG pathway is bioinformatics data for understanding high-level functions and utilities of the biological system. However, KEGG pathway analysis requires a lot of time and effort because KEGG pathways are high volume and very diverse. In this paper, we proposed a network analysis and visualization system that crawl user interest KEGG pathways, constructs a pathway network based on a hierarchy structure of pathways and visualize relations and interactions of pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our system, we evaluate the performance of our system in various experiment

Development of Korean spine database and ontology for realizing e-Spine

By 2026, Korea is expected to surpass the UN’s definition of an aged society and reach the level of a super-aged society. As a result, degenerative spinal disease and the related surgical procedures will increase exponentially. To prevent unnecessary spinal surgery and support scientific diagnosis of spinal disease and systematic prediction of treatment effects, we have been developing e-Spine which is a computerized simulation model of human spines. In this paper, we present the Korean spine database and ontology that are used as a background data for realizing e-Spine. Generally, Korean physical function is different from foreign physical function. For example, ossification of posterior longitudinal ligament is only occurred in Asians. However, developed countries are currently constructing digital human data to improve the medical and biomedical researches, while the digital human data for Korean are inadequate. Therefore, we constructed Korean spine database on Korean with normal spine or degenerative spinal diseases. To date, we have collected spine data from 77 cadavers and 298 patients. The spine data consists of 2D images such as CT, MRI, or X-ray, 3D shapes, geometry data and property data. The volume and quality of Korean spine database are now the world’s highest. Also, we constructed spinal ontology to provide a wealth of information related to spine. The spinal ontology contains anatomy of spine, method of treatment, cause, classification information related to spine. Finally, we implemented a management service for efficiently searching and managing the data. As a result, our database and ontology will offer great value and utility in the diagnosis, treatment, and rehabilitation of patients suffering from spinal diseases

CASS: A distributed network clustering algorithm based on structure similarity for large-scale network

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions