Browsing by Author "Hsieh, Sun-Yuan"

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Detecting exact breakpoints of deletions with diversity in hepatitis B viral genomic DNA from next-generation sequencing data
(2017-10-01)
Cheng, Ji-Hong
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Liu, Wen-Chun
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Chang, Ting-Tsung
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Hsieh, Sun-Yuan
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Tseng, Vincent S.
Many studies have suggested that deletions of Hepatitis B Viral (HBV) are associated with the development of progressive liver diseases, even ultimately resulting in hepatocellular carcinoma (HCC). Among the methods for detecting deletions from next-generation sequencing (NGS) data, few methods considered the characteristics of virus, such as high evolution rates and high divergence among the different HBV genomes. Sequencing high divergence HBV genome sequences using the NGS technology outputs millions of reads. Thus, detecting exact breakpoints of deletions from these big and complex data incurs very high computational cost. We proposed a novel analytical method named VirDelect (Virus Deletion Detect), which uses split read alignment base to detect exact breakpoint and diversity variable to consider high divergence in single-end reads data, such that the computational cost can be reduced without losing accuracy. We use four simulated reads datasets and two real pair-end reads datasets of HBV genome sequence to verify VirDelect accuracy by score functions. The experimental results show that VirDelect outperforms the state-of-the-art method Pindel in terms of accuracy score for all simulated datasets and VirDelect had only two base errors even in real datasets. VirDelect is also shown to deliver high accuracy in analyzing the single-end read data as well as pair-end data. VirDelect can serve as an effective and efficient bioinformatics tool for physiologists with high accuracy and efficient performance and applicable to further analysis with characteristics similar to HBV on genome length and high divergence. The software program of VirDelect can be downloaded at https://sourceforge.net/projects/virdelect/. (C) 2017 Published by Elsevier Inc.
eDRAM: Effective early disease risk assessment with matrix factorization on a large-scale medical database: A case study on rheumatoid arthritis
(2018-11-26)
Chin, Chu-Yu
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Hsieh, Sun-Yuan
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Tseng, Vincent S.
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資訊科學與工程研究所
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Institute of Computer Science and Engineering
Recently, a number of analytical approaches for probing medical databases have been developed to assist in disease risk assessment and to determine the association of a clinical condition with others, so that better and intelligent healthcare can be provided. The early assessment of disease risk is an emerging topic in medical informatics. If diseases are detected at an early stage, prognosis can be improved and medical resources can be used more efficiently. For example, if rheumatoid arthritis (RA) is detected at an early stage, appropriate medications can be used to prevent bone deterioration. In early disease risk assessment, finding important risk factors from large-scale medical databases and performing individual disease risk assessment have been challenging tasks. A number of recent studies have considered risk factor analysis approaches, such as association rule mining, sequential rule mining, regression, and expert advice. In this study, to improve disease risk assessment, machine learning and matrix factorization techniques were integrated to discover important and implicit risk factors. A novel framework is proposed that can effectively assess early disease risks, and RA is used as a case study. This framework comprises three main stages: data preprocessing, risk factor optimization, and early disease risk assessment. This is the first study integrating matrix factorization and machine learning for disease risk assessment that is applied to a nation-wide and longitudinal medical diagnostic database. In the experimental evaluations, a cohort established from a large-scale medical database was used that included 1007 RA-diagnosed patients and 921,192 control patients examined over a nine-year follow-up period (2000-2008). The evaluation results demonstrate that the proposed approach is more efficient and stable for disease risk assessment than state-of-the-art methods.
Effective Risk Assessment of Type 2 Diabetes Using Diagnostic Information Retrieval
(2018-01-01)
Chin, Chu-Yu
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Hsieh, Sun-Yuan
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Tseng, Vincent S.
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交大名義發表
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資訊工程學系
Data in electronic medical records (EMRs) have been widely employed owing to rapid advances in disease assessment technologies. Accordingly, the challenging issue of how to effectively retrieve meaningful data from large-scale medical databases for disease assessment has risen. Furthermore, the manner in which early disease risk assessment models can detect disease symptoms is an issue of concern because early detection leads to early treatment. In this paper, with the aim of detecting diseases sooner and more effectively, a novel early disease risk assessment method is proposed, and type 2 diabetes mellitus (T2DM) is used as a case study. The proposed method is to improve the quality and meaning of diagnostic data using novel features and early strategy. To apply EMRs to construct a relationship matrix between patients and diseases, a retrieval method for generalized diagnostic coded information with extracted occurrence numbers was proposed. In order to identify diseases earlier, a disease risk assessment strategy from 7, 60, and 120 days before the onset of T2DM was established. The experimental results showed that the proposed method to improve disease risk assessment achieved high accuracy in terms of AUC-ROC and AUC-PR values. These results also demonstrate that the EMR information retrieval methods play an important role for disease assessment, and assessments can be performed at an earlier stage based on large-scale diagnostic databases.
Guest Editorial: Special Section on Emerging Topics in the Design of High Performance Internet Routers
(2016-04-06)
Tseng, Yu-Chee
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Hsieh, Sun-Yuan
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資訊工程學系
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Department of Computer Science
Mining Life Patterns from Wearable Sensors Data for Elderly Anomaly Detection
(2017-01-01)
Tsai, Cheng-Hung
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Chu, Cheng-Hao
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Liu, Sun-Wei
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Hsieh, Sun-Yuan
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Tseng, Vincent S.
Life patterns can represent an individual's life style and they can help people understand their daily behavior as well as the regular habits. Discovery of life patterns has a manifold of application scenarios, which can be embedded into location based recommender systems, precise advertising, computer-aided scheduling, kind care/alert systems. In this paper, we propose an approach for life style mining with applications on elderly anomaly detection. Although there existed already studies for discovering life styles, they were mostly based on traditional single-sensor environment. Consequently, it cannot completely represent an individual's lifestyle due to the lack of sufficient information and related applications like anomaly detection cannot reach high accuracy. To deal with above-mentioned problems, our approach can mine an individual's life pattern from wearable-devices-based environment with multiple sensors. When the life patterns are applied to elderly anomaly detection, multiple-sensors-based elderly's conditions, such as physical condition and locations, are taken into considerations at the same time. For experimental evaluations, we design a data simulator to generate sensors data of elderly's daily life, based on which the effectiveness of our proposed framework is verified.
Music Recommendation Based on Information of User Profiles, Music Genres and User Ratings
(2018-01-01)
Su, Ja-Hwung
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Chin, Chu-Yu
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Yang, Hsiao-Chuan
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Tseng, Vincent S.
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Hsieh, Sun-Yuan
Music data has been becoming bigger and bigger in recent years. It makes online music stores hard to provide the users with good personalized services. Therefore, a number of past studies were proposed for effectively retrieving the user preferences on music. However, they countered problems such as new user, new item and rating sparsity. To cope with these problems, in this paper, we propose a creative method that integrates information of user profiles, music genres and user ratings. In terms of solving problem of new user, the user similarities can be calculated by the profiles instead of ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering. In terms of solving problem of rating sparsity, the unknown ratings are initialized by ratings of music genres. Even facing new music items, the rating data will not be sparse due to imputing the initialized ratings. Because the rating data is enriched, the user preference can be retrieved by item-based Collaborative Filtering. The experimental results reveal that, our proposed method performs more promising than the compared methods in terms of Root Mean Squared Error.
Special Issue on Pervasive Systems, Algorithms, and Networks Foreword
(2019-07-01)
Hsieh, Sun-Yuan
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Klasing, Ralf
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Luo, Chunbo
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Tsai, Shi-Chun
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資訊科學與工程研究所