Browsing by Author "Lu, Henry Horng-Shing"

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64-Bit and 128-bit DX random number generators
(2010-08-01)
Deng, Lih-Yuan
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Lu, Henry Horng-Shing
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Chen, Tai-Been
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統計學研究所
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Institute of Statistics
Extending 32-bit DX generators introduced by Deng and Xu (ACM Trans Model Comput Simul 13:299-309, 2003), we perform an extensive computer search for classes of 64-bit and 128-bit DX generators of large orders. The period lengths of these high resolution DX generators are ranging from 10(1915) to 10(58221). The software implementation of these generators can be developed for 64-bit or 128-bit hardware. The great empirical performances of DX generators have been confirmed by an extensive battery of tests in the TestU01 package. These high resolution DX generators can be useful to perform large scale simulations in scientific investigations for various computer systems.
A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in Young Adults
(2020-01-01)
Lin, Gen-Min
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Lu, Henry Horng-Shing
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統計學研究所
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Institute of Statistics
Objective: The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH. Method: In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm. Results: On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH. Conclusion: We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.
A gene profiling deconvolution approach to estimating immune cell composition from complex tissues
(2018-05-08)
Chen, Shu-Hwa
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Kuo, Wen-Yu
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Su, Sheng-Yao
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Chung, Wei-Chun
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Ho, Jen-Ming
Background: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. Results: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v-Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. Conclusions: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems.
A Novel Statistical Method for Automatically Partitioning Tools According to Engineers' Tolerance Control in Process Improvement
(2009-08-01)
Tu, Kevin Kai-Wen
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Lee, Jack Chao-sheng
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Lu, Henry Horng-Shing
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
In the semiconductor industry, tool comparison is a key task in yield or product quality enhancements. We develop a new method to automatically partition tools. The new method is called tolerance control partitioning (TCP). The advantages of TCP include 1) taking into account of unbalanced tool usage in manufacturing processes; 2) further partitioning these tools into several homogenous groups by related metrology results instead of detecting only the significant difference; and 3) partitioning these tools according to engineers' tolerance controls to avoid too many groups with small differences. TCP also could be applied in all similar cases such as experimental recipe or material comparisons. Therefore, using TCP, engineers could speed up yield or product quality ramping. Two simulation cases illustrate the advantages of TCP method. We also applied TCP to two real cases for yield and Cp/Cpk enhancement in the semiconductor industry. The results confirm the practical feasibility of this method.
A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex
(2016-12-09)
Yang, Shih-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Liao, Lun-De
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Lu, Henry Horng-Shing
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (RCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, RCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 +/- 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 +/- 11.73 mm), OLE (20.17 +/- 6.43 mm), PCA (19.13 +/- 0.75 mm), SFS (22.75 +/- 2.01 mm), and NN (16.75 +/- 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.
Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT
(2014-03-01)
Chen, Tai-Been
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Lu, Henry Horng-Shing
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Kim, Hang-Keun
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Son, Young-Don
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Cho, Zang-Hee
State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time. (C) 2013 Elsevier Ltd. All rights reserved.
Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis
(2018-02-01)
Chen, Peng-Jen
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Lin, Meng-Chiung
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Lai, Mei-Ju
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Lin, Jung-Chun
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Lu, Henry Horng-Shing
BACKGROUND & AIMS: Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. METHODS: We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. RESULTS: In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 +/- 0.07 seconds-shorter than the time required by experts (1.54 +/- 1.30 seconds) and nonexperts (1.77 +/- 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. CONCLUSIONS: We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images.
Analysis of the association between transcription factor binding site variants and distinct accompanying regulatory motifs in yeast
(2012-01-10)
Chiang, Sufeng
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Swamy, Krishna B. S.
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Hsu, Ting-Wei
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Tsai, Zing Tsung-Yeh
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Lu, Henry Horng-Shing
It is generally accepted that genes are regulated by the interactions between transcription factors (TFs) and their binding sites (TFBSs). Some studies have demonstrated that nucleotide variants at variable positions in TFBSs affect yeast gene regulation. Furthermore, variable positions in TFBSs in association with distinct accompanying regulatory motifs of other TFs (i.e., co-TFs) can also impact gene regulation in eukaryotes. Given that, even low-affinity IF-DNA interactions are abundant in vivo; we used both low- and high-affinity TFBSs and performed a genome-wide analysis of associations between variable positions and co-TFs. We found that in Saccharomyces cerevisiae, approximately 14% of the variable positions in TFBSs demonstrate such associations. These associations occurred in close proximity on the same promoters (i.e., highly co-localized). Moreover, such associations were highly conserved between sensu stricto yeasts and also influenced gene expression, which were consistent with enriched functional categories. (C) 2011 Elsevier B.V. All rights reserved.
Association of Tobacco Smoking with Physical Fitness of Military Males in Taiwan: The CHIEF Study
(2020-01-07)
Su, Fang-Ying
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Wang, Sheng-Huei
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Lu, Henry Horng-Shing
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Lin, Gen-Min
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統計學研究所
Tobacco smoking has been found associated with lower cardiorespiratory fitness in white and black males; however, few studies have not been conducted to clarify such relationship in Asian males. We performed a cross-sectional study to investigate the association between tobacco smoking status and physical fitness in 3,669 military males, averaged 29.4 years of age, from the cardiorespiratory fitness and hospitalization events in armed forces (CHIEF) study in Taiwan during 2014. There were 1,376 current smokers, and the others were noncurrent smokers. The effective sample size estimated was 1,230 participants, as the margin of error was +/- 3% at the 99% confidence level. Physical fitness was evaluated by time for a 3000-meter run test (aerobic fitness) and repetitive numbers of 2-minute sit-ups and 2-minute push-ups (anaerobic fitness) where all procedures were standardized by using computerized scoring systems. A multiple linear analysis adjusting for age, service specialty, body mass index, heart rate, alcohol intake, and training frequency was used to determine the relationship. As compared with noncurrent smoking, current smoking was inversely correlated with longer time for a 3000-meter run (beta = 15.66 (95% confidence intervals (CI): 10.62, 20.70)) and fewer repetitive numbers of 2-minute sit-ups and 2-minute push-ups (beta = -1.53 (95% CI: -2.08, -0.97) and -1.31 (95% CI: -2.12, -0.50), respectively). Our finding reconfirms the concept that tobacco smoking might reduce both aerobic and anaerobic fitness among young Asian males.
Boolean function network analysis of time course liver transcriptome data to reveal novel circadian transcriptional regulators in mammals
(2019-11-01)
Simak, Maria
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Lu, Henry Horng-Shing
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Yang, Jinn-Moon
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生物資訊及系統生物研究所
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統計學研究所
Background: Many biological processes in mammals are subject to circadian control at the molecular level. Disruption of circadian rhythms has been demonstrated to be associated with a wide range of diseases, such as diabetes mellitus, mental disorders, and cancer. Although the core circadian genes are well established, there are multiple reports of novel peripheral circadian regulators. The goal of this study was to provide a comprehensive computational analysis to identify novel potential circadian transcriptional regulators. Methods: To fulfill the aforementioned goal, we applied a Boolean function network method to analyze the microarray time course mouse and rat liver datasets available in the literature. The inferred direct pairwise relations were further investigated using the functional annotation tool. This approach generated a list of transcription factors (TFs) and cofactors, which were associated with significantly enriched circadian gene ontology (GO) categories. Results: As a result, we identified 93 transcriptional circadian regulators in mouse and 95 transcriptional circadian regulators in rat. Of these, 19 regulators in mouse and 21 regulators in rat were known, whereas the rest were novel. Furthermore, we validated novel circadian TFs with bioinformatics databases, previous large-scale circadian studies, and related small-scale studies. Moreover, according to predictions inferred from ChIP-Seq experiments reported in the database, 40 of our candidate circadian regulators were confirmed to have circadian genes as direct regulatory targets. In addition, we annotated candidate circadian regulators with disorders that were often associated with disruptions of circadian rhythm in the literature. Conclusion: In summary, our computational analysis, which was followed by an extensive verification by means of a literature review, can contribute to translational study from endocrinology to cancer research and provide insights for future investigation.
Budgeted Algorithm for Linearized Confidence-Weighted Learning
(2019-01-01)
Lin, Yu-Shiou
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Wu, Chung-Hao
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Lu, Henry Horng-Shing
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交大名義發表
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統計學研究所
This paper presents a novel algorithm for performing linearized confidence-weighted (LCW) learning on a fixed budget. LCW learning has been applied to solve online classification problems in recent years. To make better classification performance, it is common to combine with kernel functions through the kernel trick. However, the trick makes the LCW learning vulnerable to the curse of kernelization that causes unlimited growth in memory usage and run-time. To address this issue, we first re-interpret the LCW learning by using a resource perspective deeming every instance as a potential resource to exploit. Based on the perspective, we then propose a budgeted algorithm that approximates the LCW learning under a finite constraint on the number of available resources. The proposed algorithm enjoys finite complexities of time and space and thus is able to break the curse. Experiments on several open datasets show that the proposed algorithm approximates the LCW learning well and is competitive to state-of-the-art budgeted algorithms.
Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet
(2019-01-01)
Almuhayar, Mawanda
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Lu, Henry Horng-Shing
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Iriawan, Nur
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統計學研究所
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Institute of Statistics
Deep learning development nowadays has attracted a lot of attention because of its effectiveness and good performance. The performance of deep learning in medical images analysis already can compete with medical image experts. However, there are experts that still believe deep learning only efficient for the big datasets, because of deep learning performance in small datasets still not satisfying enough. In this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest X-ray images with a relatively small dataset. We classify chest X-ray into a binary classification which is a normal image and image with abnormalities. We built and experimented our model using the public dataset of Shenzen Hospital dataset. We also use a different type of input based on different images preprocessing so that the model can perform accurate classification. Based on the result, pre-trained CheXNet with a newly trained fully connected network on the cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain region inside the images, such as other regions outside the lung region and black colored region outside the body region.
Clonal spread of carbapenem-resistant Acinetobacter baumannii across a community hospital and its affiliated long-term care facilities: A cross sectional study
(2018-06-01)
Chen, Chang-Hua
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Kuo, Han-Yueh
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Hsu, Po-Jui
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Chang, Chien-Min
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Chen, Jiann-Yuan
Background: The global spread of carbapenem-resistant Acinetobacter baumannii (CRAB) is now a public health problem. In Taiwan, the relationship of the CRAB circulation between long-term care facilities (LTCFs) and acute care hospitals remains unclear. Here, we use molecular epidemiologic methods to describe the transmission of CRAB isolates between a community hospital and its affiliated LTCFs. Methods: Subjects localized in eight LTCFs who were not admitted acute care hospitals in recent a year were enrolled in this study. CRAB isolates were collected during June 1, 2015 and December 31, 2015. DNA fingerprinting was performed by repetitive extragenic palindromic sequence-based polymerase chain reaction (Rep-PCR) and multilocus sequence typing (MLST). Multiplex-PCR amplification for the detection of bla(OXA) genes and beta-lactamase genes was performed. Results: Twenty one subjects were enrolled. The major hospital admission diagnoses among the 21 subjects were pneumonia (71.4%). Genotyping of CRAB isolates by Rep-PCR revealed that a major clone, designated as type III, comprised fifteen of 21 (71.4%) isolates taken from 5 LTCFs and one study hospital. The isolates with type III were subtyped by PubMLST into 4 ST types. The most prevalent bla(OXA) genes in these isolates were bla(OXA-23)-like (85.70%, 18/21). Twenty isolates carried bla(SHV). Conclusion: Clonal spread of bla(OXA-23)-carrying CRABs was found around LTCFs and the affiliated hospital. In Taiwan, it is important for the government to focus attention on the importance of identifying and tracing CRAB infections in LTCFs. Copyright (C) 2017, Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC.
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.
Dynamic change of surface microbiota with different environmental cleaning methods between two wards in a hospital
(2017-01)
Chen, Chang-Hua
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Tu, Chi-Chao
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Kuo, Han-Yueh
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Zeng, Rong-Fong
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Yu, Cheng-Sheng
Terminal disinfection and daily cleaning have been performed in hospitals in Taiwan for many years to reduce the risks of healthcare-associated infections. However, the effectiveness of these cleaning approaches and dynamic changes of surface microbiota upon cleaning remain unclear. Here, we report the surface changes of bacterial communities with terminal disinfection and daily cleaning in a medical intensive care unit (MICU) and only terminal disinfection in a respiratory care center (RCC) using 16s ribosomal RNA (rRNA) metagenomics. A total of 36 samples, including 9 samples per sampling time, from each ward were analysed. The clinical isolates were recorded during the sampling time. A large amount of microbial diversity was detected, and human skin microbiota (HSM) was predominant in both wards. In addition, the colonization rate of the HSM in the MICU was higher than that in the RCC, especially for Moraxellaceae. A higher alpha-diversity (p = 0.005519) and a lower UniFrac distance was shown in the RCC due to the lack of daily cleaning. Moreover, a significantly higher abundance among Acinetobacter sp., Streptococcus sp. and Pseudomonas sp. was shown in the RCC compared to the MICU using the paired t test. We concluded that cleaning changes might contribute to the difference in diversity between two wards.
Efficient computer search of large-order multiple recursive pseudo-random number generators
(2012-07-01)
Deng, Lih-Yuan
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Shiau, Jyh-Jen Horng
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Lu, Henry Horng-Shing
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統計學研究所
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Institute of Statistics
Utilizing some results in number theory, we propose an efficient method to speed up the computer search of large-order maximum-period Multiple Recursive Generators (MRGs). We conduct the computer search and identify many efficient and portable MRGs of order up to 25,013, which have the equi-distribution property in up to 25,013 dimensions and the period lengths up to 10(233,361) approximately. In addition, a theoretical test is adopted to further evaluate and compare these generators. An extensive empirical study shows that these generators behave well when tested with the stringent Crush battery of the test package TestU01. (C) 2012 Elsevier B.V. All rights reserved.
Evolution of Domain Architectures and Catalytic Functions of Enzymes in Metabolic Systems
(2012)
Suen, Summit
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Lu, Henry Horng-Shing
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Yeang, Chen-Hsiang
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統計學研究所
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Institute of Statistics
Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes. The reconstructed history reveals distinct patterns of evolutionary mechanisms between prokaryotic and eukaryotic networks. Although the evolutionary mechanisms in early ancestors of prokaryotes and eukaryotes are quite similar, more novel and duplicated domain compositions with identical catalytic functions arise along the eukaryotic lineage. In contrast, prokaryotic enzymes become more versatile by catalyzing multiple reactions with similar chemical operations. Moreover, different metabolic pathways are enriched with distinct network evolution mechanisms. For instance, although the pathways of steroid biosynthesis, protein kinases, and glycosaminoglycan biosynthesis all constitute prominent features of animal-specific physiology, their evolution of domain architectures and catalytic functions follows distinct patterns. Steroid biosynthesis is enriched with reaction creations but retains a relatively conserved repertoire of domain compositions and proteins. Protein kinases retain conserved reactions but possess many novel domains and proteins. In contrast, glycosaminoglycan biosynthesis has high rates of reaction/protein creations and domain recruitments. Finally, we elicit and validate two general principles underlying the evolution of DPR networks: 1) duplicated enzyme proteins possess similar catalytic functions and 2) the majority of novel domains arise to catalyze novel reactions. These results shed new lights on the evolution of metabolic systems.
Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae
(2017-10-05)
Simak, Maria
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Yeang, Chen-Hsiang
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Lu, Henry Horng-Shing
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統計學研究所
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Institute of Statistics
The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN). For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN) model based on techniques of hidden Markov model (HMM), likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.
Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae (vol 12, e0185475, 2017)
(2019-08-22)
Simak, Maria
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Yeang, Chen-Hsiang
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Lu, Henry Horng-Shing
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生物資訊及系統生物研究所
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Institude of Bioinformatics and Systems Biology
First-ever ischemic stroke in elderly patients: predictors of functional outcome following carotid artery stenting
(2016-01-01)
Lin, Chih-Ming
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Chang, Yu-Jun
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Liu, Chi-Kuang
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Yu, Cheng-Sheng
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Lu, Henry Horng-Shing
Age is an important risk factor for stroke, and carotid artery stenosis is the primary cause of first-ever ischemic stroke. Timely intervention with stenting procedures can effectively prevent secondary stroke; however, the impact of stenting on various periprocedural physical functionalities has never been thoroughly investigated. The primary aim of this study was to investigate whether prestenting characteristics were associated with long-term functional outcomes in patients presenting with first-ever ischemic stroke. The secondary aim was to investigate whether patient age was an important factor in outcomes following stenting, measured by the modified Rankin scale (mRS). In total, 144 consecutive patients with first-ever ischemic stroke who underwent carotid artery stenting from January 2010 to November 2014 were included. Clinical data were obtained by review of medical records. The Barthel index (BI) and mRS were used to assess disability before stenting and at 12-month follow-up. In total, 72/144 patients showed improvement (mRS[+]), 71 showed stationary and one showed deterioration in condition (mRS[-]). The prestenting parameters, ratio of cerebral blood volume (1.41 vs 1.2 for mRS[-] vs mRS[+]), BI ( 75 vs 85), and high-sensitivity C-reactive protein (hsCRP 5.0 vs 3.99), differed significantly between the two outcome groups (P<0.05). The internal carotid artery/common carotid artery ratio (P=0.011), BI (P=0.019), ipsilateral internal carotid artery resistance index (P=0.003), and HbA1c (P=0.039) were all factors significantly associated with patient age group. There was no significant association between age and poststenting outcome measured by mRS with 57% of patients in the >= 75 years age group showing mRS(-) and 43% showing mRS(+) (P=0.371). Our findings indicate that in our elderly patient series, carotid artery stenting may benefit a significant proportion of carotid stenotic patients regardless of age. Ratio of cerebral blood volume, BI, and admission hsCRP could serve as important predictors of mRS improvement and may facilitate differentiation of patients at baseline.