Browsing by Author "Chueh, Tung-Hung"

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Association Between Chronic Hepatitis C Virus Infection and Bone Mineral Density
(2012-12-01)
Lin, Jung-Chun
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Hsieh, Tsai-Yuan
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Wu, Chia-Chun
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Chen, Peng-Jen
;
Chueh, Tung-Hung
Whether chronic hepatitis C virus (HCV) infection is a risk factor for the development of bone disease has long been controversial. For this reason, chronic HCV-infected participants (n = 69) were recruited into a prospective cohort study and underwent dual-energy X-ray absorptiometry for determination of bone mineral density (BMD). Fibrosis staging was evaluated according to the noninvasive index FIB-4. T scores at the femoral neck and lumbar spine were used as the primary outcome variables to assess the association between degree of liver disease and BMD. The study cohort was 41 % male with a mean age of 53.6 years. The mean BMD, Z score, and T score values of lumbar spine in chronic hepatitis C (CHC) patients were significantly lower than those in healthy controls (p < 0.001). The rate of osteoporosis for CHC patients aged 45-54 years was significantly higher than that of the control group (p = 0.011). Bone alkaline phosphatase and C-terminal cross-linking telopeptide of type I collagen levels were also significantly higher in CHC patients with reduced BMD. Patients with more advanced liver fibrosis had significantly lower BMD. In conclusion, reduced BMD is common in this population of chronic HCV-infected patients and associated with liver disease severity. This extrahepatic manifestation is probably secondary to increased bone turnover in osteodystrophy pathogenesis.
Changes in serum vitamin D levels during therapy for chronic hepatitis C virus infection
(2018-11-01)
Lin, Jung-Chun
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Chueh, Tung-Hung
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Lin, Hsuan-Hwai
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Chen, Peng-Jen
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Shih, Yu-Lueng
Clinical Characteristics of Early Onset Pancreatic Adenocarcinoma A Medical Center Experience and Review of the Literature
(2011-05-01)
Lin, Jung-Chun
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Chan, De-Chuan
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Chen, Peng-Jen
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Chu, Heng-Cheng
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Chueh, Tung-Hung
Constructing Biological Pathways by a Two-Step Counting Approach
(2011-06-01)
Wang, Hsiuying
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Lu, Henry Horng-Shing
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Chueh, Tung-Hung
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統計學研究所
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Institute of Statistics
Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network.
Effect of Chronic Hepatitis C Virus Infection on Bone Mineral Density
(2012-10-01)
Lin, Jung-Chun
;
Hsieh, Tsai-Yuan
;
Chueh, Tung-Hung
;
Chen, Peng-Jen
;
Chang, Wei-Kuo
Effect of Chronic Hepatitis C Virus Infection on Bone Mineral Density
(2012-10-01)
Lin, Jung-Chun
;
Hsieh, Tsai-Yuan
;
Chueh, Tung-Hung
;
Chen, Peng-Jen
;
Chang, Wei-Kuo
Inference of Biological Pathway from Gene Expression Profiles by Time Delay Boolean Networks
(2012-08-31)
Chueh, Tung-Hung
;
Lu, Henry Horng-Shing
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統計學研究所
;
Institute of Statistics
One great challenge of genomic research is to efficiently and accurately identify complex gene regulatory networks. The development of high-throughput technologies provides numerous experimental data such as DNA sequences, protein sequence, and RNA expression profiles makes it possible to study interactions and regulations among genes or other substance in an organism. However, it is crucial to make inference of genetic regulatory networks from gene expression profiles and protein interaction data for systems biology. This study will develop a new approach to reconstruct time delay Boolean networks as a tool for exploring biological pathways. In the inference strategy, we will compare all pairs of input genes in those basic relationships by their corresponding p-scores for every output gene. Then, we will combine those consistent relationships to reveal the most probable relationship and reconstruct the genetic network. Specifically, we will prove that O(log n) state transition pairs are sufficient and necessary to reconstruct the time delay Boolean network of n nodes with high accuracy if the number of input genes to each gene is bounded. We also have implemented this method on simulated and empirical yeast gene expression data sets. The test results show that this proposed method is extensible for realistic networks.
STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING
(2012-06-01)
Chueh, Tung-Hung
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Chen, Tai-Been
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Lu, Henry Horng-Shing
;
Ju, Shan-Shan
;
Tao, Teh-Ho
For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system.