Browsing by Author "Hu, Yuh-Jyh"

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A Fast Window-based Scalar Multiplication Algorithm for Elliptic Curve Cryptography in Wireless Sensor Networks
(2015-01-01)
Ye, Hung-Nan
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Wang, Kuochen
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Jan, Rong-Hong
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Hu, Yuh-Jyh
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Tseng, Yu-Chee
In this paper, we propose an enhanced window-based mutual opposite form (EW-MOF) for scalar multiplication with ECC in WSNs. The proposed EW-MOF combines MOF with an enhanced window method that can reduce not only pre-computation time and memory usage, but also average key generation time that includes pre-computation time in each sensor node. Our analysis has shown that the proposed EW-MOF requires a smaller number of essential pre-computed points than the one's complement. Therefore, it is very suitable for WSNs. Simulation results show that the proposed EW-MOF is 24.69% faster than the one's complement method, which is the best method available, in the average key generation time of ECC that includes pre-computation time under different field sizes. In summary, the proposed EW-MOF is more feasible than the one's complement for wireless sensor networks in terms of key generation time and power saving.
A Meta Decision Tree Approach for B-cell Epitope Mining
(2016-01-01)
Hu, Yuh-Jyh
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You, Shun-Ning
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分子醫學與生物工程研究所
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資訊工程學系
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Institute of Molecular Medicine and Bioengineering
The ability of antibodies to respond to an antigen depends on the antibodies' specific recognition of epitopes, which are sites of the antigen to which antibodies bind. An increase in the availability of protein sequences and structures has enabled the identification of conformational epitopes, using various computational methods. The meta learner, among various approaches, has proved its feasibility and comparable accuracy in B-cell epitope prediction in previous studies. Nevertheless, its performance highly depends on the classification results of its multiple epitope base predictors within the meta learning architecture. We here propose bagging meta decision trees for epitope prediction to avoid the dependence on epitope prediction tools, and introduce 3D sphere-based attributes to improve prediction accuracy. Our experimental results demonstrate the superior performance of the bagging meta decision tree approach in comparison with single epitope predictors.
A meta-learning approach for B-cell conformational epitope prediction
(2014-11-18)
Hu, Yuh-Jyh
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Lin, Shun-Chien
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Lin, Yu-Lung
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Lin, Kuan-Hui
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You, Shun-Ning
Background: One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains. Results: We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor. Conclusions: Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.
A Similarity-Based Learning Algorithm Using Distance Transformation
(2015-06-01)
Hu, Yuh-Jyh
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Yu, Min-Che
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Wang, Hsiang-An
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Ting, Zih-Yun
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交大工研院聯合研發中心
Numerous theories and algorithms have been developed to solve vectorial data learning problems by searching for the hypothesis that best fits the observed training sample. However, many real-world applications involve samples that are not described as feature vectors, but as (dis) similarity data. Converting vectorial data into (dis) similarity data is more easily performed than converting (dis) similarity data into vectorial data. This study proposes a stochastic iterative distance transformation model for similarity-based learning. The proposed model can be used to identify a clear class boundary in data by modifying the (dis) similarities between examples. The experimental results indicate that the performance of the proposed method is comparable with those of various vector-based and proximity-based learning algorithms.
A Two-Stage Learning Method for Response Prediction
(2015)
Chen, Kuan-Hsi
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Ting, Zih-Yun
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Shen, Jia-Ying
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Hu, Yuh-Jyh
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Liang, Tyne
Social networks have become a popular and powerful communication platform as the mobile technology evolves. To evaluate the influence of a message on a social network, response prediction is crucial in modeling the message propagation and interaction among users. To predict whether a new message will receive responses, we propose a two-stage learning method using a new set of features derived from the messages, the users and their responding behaviors. This method first clusters the messages, and then learns the different prediction models from the clusters respectively. The central argument for this two-stage strategy is that the classifiers trained separately from the clustered data sets can focus on particular types of data, reduce the effects of noise, and consequently have an overall higher predictive performance than a single classifier trained from the entire data set. We tested the proposed two-stage learner on Plurk, and compared it with other classifiers. The experimental results show that the two-stage learner outperformed the gradient boosting decision tree learner, the logistic function learner, and the support vector machine for not only the predictive accuracy, but also for the efficiency.
Applying Stacked and Cascade Generalizations to B-cell Epitope Prediction
(2014-01-01)
Hu, Yuh-Jyh
;
Lin, Shun-Chien
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Lin, Yu-Lung
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分子醫學與生物工程研究所
;
Institute of Molecular Medicine and Bioengineering
One of the major challenges in the field of vaccine design is to identify the B-cell epitopes in ever-evolving viruses. Various prediction servers have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose meta learning approaches to epitope prediction based on stacked generalization and cascade generalization. By combining the base prediction servers in a hierarchical architecture, we demonstrated that a meta learner outperformed the best single server in predicting the epitopes of an independent dataset of pathogen proteins.
Bicluster analysis of genome-wide gene expression
(2006)
Chen, Kuanchung
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Hu, Yuh-Jyh
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資訊工程學系
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Department of Computer Science
A number of biclustering approaches have been developed to mitigate the limitations of standard clustering algorithms. They have different problem formulation, search strategy and computational complexity. We proposed a new biclustering method based on the framework of market basket analysis in which a bicluster is described as a frequent itemset. As a feasibility test, we compared it with several standard clustering algorithms on a genome-wide yeast microarray dataset, and it showed very promising results. We later did a comparison between our approach and various current biclustering methods, following a systematic evaluation procedure recently published. The experimental results demonstrate that our new method outperforms the others.
Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
(2012-11-14)
Hu, Yuh-Jyh
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Ku, Tien-Hsiung
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Jan, Rong-Hong
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Wang, Kuochen
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Tseng, Yu-Chee
Background: Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods: The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results: The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naive Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion: This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.
Discovery of Structural Motifs Using Protein Structural Alphabets and 1D Motif-Finding Methods
(2010)
Ku, Shih-Yen
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Hu, Yuh-Jyh
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電控工程研究所
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Institute of Electrical and Control Engineering
Although the increasing number of available 3D proteins structures has made a wide variety of computational protein structure research possible, yet the success is still hindered by the high 3D computational complexity. Based on 3D information, several 1D protein structural alphabets have been developed, which can not only describe the global folding structure of a protein as a 1D sequence, but can also characterize local structures in proteins. Instead of applying computationally intensive 3D structure alignment tools, we introduce an approach that combines standard 1D motif detection methods with structural alphabets to discover locally conserved protein motifs. These 1D structural motifs can characterize protein groups at different levels, e.g., families, super families, and folds in SCOP, as group features.
Evolutionary conservation of DNA-contact residues in DNA-binding domains
(2008)
Chang, Yao-Lin
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Tsai, Huai-Kuang
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Kao, Cheng-Yan
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Chen, Yung-Chian
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Hu, Yuh-Jyh
Background: DNA-binding proteins are of utmost importance to gene regulation. The identification of DNA-binding domains is useful for understanding the regulation mechanisms of DNA-binding proteins. In this study, we proposed a method to determine whether a domain or a protein can has DNA binding capability by considering evolutionary conservation of DNA-binding residues. Results: Our method achieves high precision and recall for 66 families of DNA-binding domains, with a false positive rate less than 5% for 250 non-DNA-binding proteins. In addition, experimental results show that our method is able to identify the different DNA-binding behaviors of proteins in the same SCOP family based on the use of evolutionary conservation of DNA-contact residues. Conclusion: This study shows the conservation of DNA-contact residues in DNA-binding domains. We conclude that the members in the same subfamily bind DNA specifically and the members in different subfamilies often recognize different DNA targets. Additionally, we observe the co-evolution of DNA-contact residues and interacting DNA base-pairs.
Extracting the abstraction pyramid from complex networks
(2010-08-03)
Cheng, Chia-Ying
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Hu, Yuh-Jyh
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分子醫學與生物工程研究所
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資訊工程學系
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Institute of Molecular Medicine and Bioengineering
Background: At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e. g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e. g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships. Results: We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the inclusion (i.e., part-of) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems. Conclusions: Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at http://140.113.166.165/pyramabs.php/.
Identification and Chronological Analysis of Genomic Signatures in Influenza A Viruses
(2014-01-08)
Hu, Yuh-Jyh
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Tu, Po-Chin
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Lin, Chun-Sheng
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Guo, Szu-Ting
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分子醫學與生物工程研究所
An increase in the availability of data on the influenza A viruses (IAV) has enabled the identification of the potential determinants of IAV host specificity using computational approaches. In this study, we proposed an alternative approach, based on the adjusted Rand index (ARI), for the evaluation of genomic signatures of IAVs and their ability to distinguish hosts they infected. Our experiments showed that the host-specific signatures identified using the ARI were more characteristic of their hosts than those identified using previous measures. Our results provided updates on the host-specific genomic signatures in the internal proteins of the IAV based on the sequence data as of February 2013 in the National Center for Biotechnology Information (NCBI). Unlike other approaches for signature recognition, our approach considered not only the ability of signatures to distinguish hosts (according to the ARI), but also the chronological relationships among proteins. We identified novel signatures that could be mapped to known functional domains, and introduced a chronological analysis to investigate the changes in host-specific genomic signatures over time. Our chronological analytical approach provided results on the adaptive variability of signatures, which correlated with previous studies' findings, and indicated prospective adaptation trends that warrant further investigation.
iPCA: an integration information system for patient controlled analgesia using wireless techniques
(2013)
Yang, Shu-Fen
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Ku, Tien-Hsiung
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Jeng, Andy An-Kai
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Jan, Rong-Hong
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Tseng, Yu-Chee
Recently, pain management has become an important aspect of medical care. Patient Controlled Analgesia (PCA) is a particularly effective and flexible treatment that allows patients to adjust the dosages of the analgesics that control their pain after surgery. However, PCA requires manual operation for sustained attention, and should be analysed with other pathology information for synergic usage. In this paper, we propose an integrated information system for pain management, called Integrated Patient Controlled Analgesia (iPCA). The system integrates various PCA-related devices by wireless techniques to provide real-time information for medial staffs, and offer more efficient and convenient pain control for both in-hospital and at-home patients. To evaluate our system, we have developed a prototype and conducted a series of field trails at a large private hospital in southern Taiwan. The results show that the iPCA system indeed works well with patients, and can provide better post-operative pain control by efficiently collecting PCA usage information.
Pattern discovery from patient controlled analgesia demand behavior
(2012-10-01)
Hu, Yuh-Jyh
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Ku, Tien-Hsiung
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分子醫學與生物工程研究所
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資訊工程學系
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Institute of Molecular Medicine and Bioengineering
Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24 h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise regression and Bayes risk analysis to evaluate the influence of demand pattern on analgesic requirements. We identify three demand patterns from 1655 patient controlled analgesia request log files. Statistical tests show correlations of gender (p=.0022), diastolic blood pressure (p =.025), surgery type (p =.0028), and surgical duration (p <.0095) with demand patterns. Stepwise regression and Bayes risk analysis show demand pattern plays the most important role in analgesic consumption prediction (p=0.E+0). This study suggests analgesia request patterns over time exist among patients, and clustering can disclose demand behavioral patterns. (C) 2012 Elsevier Ltd. All rights reserved.
Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach
(2018-01-01)
Hu, Yuh-Jyh
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Ku, Tien-Hsiung
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Yang, Yu-Hung
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Shen, Jia-Ying
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分子醫學與生物工程研究所
Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.
Protein structure search and local structure characterization
(2008-08-22)
Ku, Shih-Yen
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Hu, Yuh-Jyh
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分子醫學與生物工程研究所
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資訊工程學系
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Institute of Molecular Medicine and Bioengineering
Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA. Results: We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at http://140.113.166.178/safast/. Conclusion: The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.
Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme
(2019-06-10)
Chen, Kuan-Hsi
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Wang, Tsai-Feng
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Hu, Yuh-Jyh
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交大名義發表
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分子醫學與生物工程研究所
BackgroundAlthough various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.ResultsWe developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors.ConclusionWe introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.
Structural alphabet motif discovery and a structural motif database
(2012-01-01)
Ku, Shih-Yen
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Hu, Yuh-Jyh
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分子醫學與生物工程研究所
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資訊工程學系
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Institute of Molecular Medicine and Bioengineering
This study proposes a general framework for structural motif discovery. The framework is based on a modular design in which the system components can be modified or replaced independently to increase its applicability to various studies. It is a two-stage approach that first converts protein 3D structures into structural alphabet sequences, and then applies a sequence motif-finding tool to these sequences to detect conserved motifs. We named the structural motif database we built the SA-Motifbase, which provides the structural information conserved at different hierarchical levels in SCOP. For each motif, SA-Motifbase presents its 3D view; alphabet letter preference; alphabet letter frequency distribution; and the significance. SA-Motifbase is available at http://bioinfo.cis.nctu.edu.tw/samotifbase/. (C) 2011 Elsevier Ltd. All rights reserved.