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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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    Diversity and enterotype in gut bacterial community of adults in Taiwan
    (2017-01-01)
    Liang, Chao
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    Tseng, Han-Chi
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    Chen, Hui-Mei
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    Wang, Wei-Chi
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    Chiu, Chih-Min
    Background: Gastrointestinal microbiota, particularly gut microbiota, is associated with human health. The biodiversity of gut microbiota is affected by ethnicities and environmental factors such as dietary habits or medicine intake, and three enterotypes of the human gut microbiome were announced in 2011. These enterotypes are not significantly correlated with gender, age, or body weight but are influenced by long-term dietary habits. However, to date, only two enterotypes (predominantly consisting of Bacteroides and Prevotella) have shown these characteristics in previous research; the third enterotype remains ambiguous. Understanding the enterotypes can improve the knowledge of the relationship between microbiota and human health. Results: We obtained 181 human fecal samples from adults in Taiwan. Microbiota compositions were analyzed using next-generation sequencing (NGS) technology, which is a culture-independent method of constructing microbial community profiles by sequencing 16S ribosomal DNA (rDNA). In these samples, 17,675,898 sequencing reads were sequenced, and on average, 215 operational taxonomic units (OTUs) were identified for each sample. In this study, the major bacteria in the enterotypes identified from the fecal samples were Bacteroides, Prevotella, and Enterobacteriaceae, and their correlation with dietary habits was confirmed. A microbial interaction network in the gut was observed on the basis of the amount of short-chain fatty acids, pH value of the intestine, and composition of the bacterial community (enterotypes). Finally, a decision tree was derived to provide a predictive model for the three enterotypes. The accuracies of this model in training and independent testing sets were 97.2 and 84.0%, respectively. Conclusions: We used NGS technology to characterize the microbiota and constructed a predictive model. The most significant finding was that Enterobacteriaceae, the predominant subtype, could be a new subtype of enterotypes in the Asian population.
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    A predictive model for acute allograft rejection of liver transplantation
    (2018-03-15)
    Liu, Chien-Liang
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    Soong, Ruey-Shyang
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    Lee, Wei-Chen
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    Chen, De-Hsuan
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    Hsu, Shang-Hwa
    Orthotopic liver transplantation (OLT) has become an increasingly used treatment for end-stage liver disease. However, acute allograft rejection is still a problem in postoperative care of liver transplantation with immunosuppressive therapy and it can lead to allograft damage and harm the survival of liver transplantation patient. This work proposes to use data-driven approach to build a predictive model for acute rejection. We consider not only prediction accuracy, but also interpretability of the prediction outcome in building the predictive model, so that the medical staffs can identify how the prediction is induced from data. The experiments use the real data provided by liver transplantation intensive care unit (ICU) of Chang Gung Memorial Hospital, Taiwan. In this work, the data is from a medical center, in which the patient data ranges from 2004 to 2013, and the number of data records is approximately 2 million. To the best of our knowledge, this is the first work using a large-scale database to focus on liver transplantation and generate interpretable rules that could be used by medical staffs. We compare with several methods, including SVM, ANN and random forest, and the experimental results indicate that the proposed method is comparative, and provides interpretable results. Central to the proposed method is to consider interpretability, and the goal is to provide interpretable results for the medical staffs to make decisions. The proposed transformation algorithms belong to data-driven approaches, so they could be applied to other intelligent or expert systems. Moreover, the outcomes are presented in rule format, which could be used by medical staffs and other expert systems. (C) 2017 Elsevier Ltd. All rights reserved.