Browsing by Author "Ho, Shinn-Ying"

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A hydrophobic spine stabilizes a surface-exposed alpha-helix according to analysis of the solvent-accessible surface area
(2016-12-22)
Liou, Yi-Fan
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Huang, Hui-Ling
;
Ho, Shinn-Ying
;
生物科技學系
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生物資訊及系統生物研究所
Background: Most of hydrophilic and hydrophobic residues are thought to be exposed and buried in proteins, respectively. In contrast to the majority of the existing studies on protein folding characteristics using protein structures, in this study, our aim was to design predictors for estimating relative solvent accessibility (RSA) of amino acid residues to discover protein folding characteristics from sequences. Methods: The proposed 20 real-value RSA predictors were designed on the basis of the support vector regression method with a set of informative physicochemical properties (PCPs) obtained by means of an optimal feature selection algorithm. Then, molecular dynamics simulations were performed for validating the knowledge discovered by analysis of the selected PCPs. Results: The RSA predictors had the mean absolute error of 14.11% and a correlation coefficient of 0.69, better than the existing predictors. The hydrophilic-residue predictors preferred PCPs of buried amino acid residues to PCPs of exposed ones as prediction features. A hydrophobic spine composed of exposed hydrophobic residues of an a-helix was discovered by analyzing the PCPs of RSA predictors corresponding to hydrophobic residues. For example, the results of a molecular dynamics simulation of wild-type sequences and their mutants showed that proteins 1MOF and 2WRP_H16I (Protein Data Bank IDs), which have a perfectly hydrophobic spine, have more stable structures than 1MOF_I54D and 2WRP do (which do not have a perfectly hydrophobic spine). Conclusions: We identified informative PCPs to design high-performance RSA predictors and to analyze these PCPs for identification of novel protein folding characteristics. A hydrophobic spine in a protein can help to stabilize exposed alpha-helices.
A hydrophobic spine stabilizes a surface-exposed alpha-helix according to analysis of the solvent-accessible surface area
(2016-12-22)
Liou, Yi-Fan
;
Huang, Hui-Ling
;
Ho, Shinn-Ying
;
生物科技學系
;
生物資訊及系統生物研究所
Background: Most of hydrophilic and hydrophobic residues are thought to be exposed and buried in proteins, respectively. In contrast to the majority of the existing studies on protein folding characteristics using protein structures, in this study, our aim was to design predictors for estimating relative solvent accessibility (RSA) of amino acid residues to discover protein folding characteristics from sequences. Methods: The proposed 20 real-value RSA predictors were designed on the basis of the support vector regression method with a set of informative physicochemical properties (PCPs) obtained by means of an optimal feature selection algorithm. Then, molecular dynamics simulations were performed for validating the knowledge discovered by analysis of the selected PCPs. Results: The RSA predictors had the mean absolute error of 14.11% and a correlation coefficient of 0.69, better than the existing predictors. The hydrophilic-residue predictors preferred PCPs of buried amino acid residues to PCPs of exposed ones as prediction features. A hydrophobic spine composed of exposed hydrophobic residues of an a-helix was discovered by analyzing the PCPs of RSA predictors corresponding to hydrophobic residues. For example, the results of a molecular dynamics simulation of wild-type sequences and their mutants showed that proteins 1MOF and 2WRP_H16I (Protein Data Bank IDs), which have a perfectly hydrophobic spine, have more stable structures than 1MOF_I54D and 2WRP do (which do not have a perfectly hydrophobic spine). Conclusions: We identified informative PCPs to design high-performance RSA predictors and to analyze these PCPs for identification of novel protein folding characteristics. A hydrophobic spine in a protein can help to stabilize exposed alpha-helices.
A novel intelligent multiobjective simulated annealing algorithm for designing robust PID controllers
(2008-03-01)
Hung, Ming-Hao
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Shu, Li-Sun
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Ho, Shinn-Jang
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Hwang, Shiow-Fen
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Ho, Shinn-Ying
This paper proposes an intelligent multiobjective simulated annealing algorithm (IMOSA) and its application to an optimal proportional integral derivative (PID) controller design problem. A well-designed PID-type controller should satisfy the following objectives: 1) disturbance attenuation; 2) robust stability; and 3) accurate setpoint tracking. The optimal PID controller design problem is a large-scale multiobjective optimization problem characterized by the following: 1) nonlinear multimodal search space; 2) large-scale search space; 3) three tight constraints; 4) multiple objectives; and 5) expensive objective function evaluations. In contrast to existing multiobjective algorithms of simulated annealing, the high performance in IMOSA arises mainly from a novel multiobjective generation mechanism using a Pareto-based scoring function without using heuristics. The multiobjective generation mechanism operates on a high-score nondominated solution using a systematic reasoning method based on an orthogonal experimental design, which exploits its neighborhood to economically generate a set of well-distributed nondominated solutions by, considering individual and overall objectives. IMOSA is evaluated by using a practical design example of a super-maneuverable fighter aircraft system. An efficient existing multiobjective algorithm, the improved strength Pareto evolutionary algorithm, is also applied to the same example for comparison. Simulation results demonstrate high performance of the IMOSA-based method in designing robust PID controllers.
Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method
(2007-09-01)
Huang, Wen-Lin
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Chen, Hung-Ming
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Hwang, Shiow-Fen
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Ho, Shinn-Ying
;
生物科技學系
Amphiphilic pseudo-amino acid composition (Am-Pse-AAC) with extra sequence-order information is a useful feature for representing enzymes. This study first utilizes the k-nearest neighbor (k-NN) rule to analyze the distribution of enzymes in the Am-Pse-AAC feature space. This analysis indicates the distributions of multiple classes of enzymes are highly overlapped. To cope with the overlap problem, this study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified. The fuzzy membership values of a query sample Q are dynamically determined according to the position of Q and its weighted distances to the k nearest neighbors. Using the same enzymes of the oxidoreductases family for comparisons, the prediction accuracy of AFK-NN is 76.6%, which is better than those of Support Vector Machine (73.6%), the decision tree method C5.0 (75.4%) and the existing covariant-discriminate algorithm (70.6%) using a jackknife test. To evaluate the generalization ability of AFK-NN, the datasets for all six families of entirely sequenced enzymes are established from the newly updated SWISS-PROT and ENZYME database. The accuracy of AFK-NN on the new large-scale dataset of oxidoreductases family is 83.3%, and the mean accuracy of the six families is 92.1 %. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles
(2007-10-01)
Ho, Shinn-Ying
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Hsieh, Chih-Hung
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Yu, Fu-Chieh
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Huang, Hui-Ling
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生物資訊及系統生物研究所
From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. However, the inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of nonlinear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with the curse of dimensionality, the proposed algorithm consists of two stages, where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on an orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems, 2) IGA is significantly superior to the existing method GA with simplex crossover (SPXGA), and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.
Angiotensin Receptor Blockers Decrease the Risk of Major Adverse Cardiovascular Events in Patients with End-Stage Renal Disease on Maintenance Dialysis: A Nationwide Matched-Cohort Study
(2015-10-21)
Tzeng, Nian-Sheng
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Yin, Yun-Ju
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Li, Chien-Hsun
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Chen, Hung-An
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Chiu, Shih-Hsiang
Background Major adverse cardiovascular events (MACE) cause the leading cause of morbidity and mortality in patients with end-stage renal disease (ESRD) on maintenance Hemodialysis (HD) or peritoneal dialysis (PD). Many randomized-controlled trials (RCTs) have proved that angiotensin receptor blockers (ARBs) can reduce the risk of MACE in the people with normal or impaired kidney function without dialysis. This study seeks to clarify whether ARBs therapy could also attenuate this risk in patients with ESRD on maintenance dialysis. Materials and Methods The National Health Research Institute provided a database of one million random subjects for the study. A random sample was taken of 1800 patients >= 18 years y/o with ESRD on dialysis without a history of MACE and use of ARBs within 6-months prior to enrollment. Cox proportional hazard regression analysis was used to identify the risk factors and compute the hazard ratios accompanying 95% confidence intervals. Results In these 1800 patients, 1061 had never used ARBs, while 224 had used them for 1-90 days, and 515 had used them for more than 90 days. We found that ARBs significantly decrease the incidences of acute myocardial infarctions (AMI), coronary artery diseases (CAD) requiring coronary stent or percutaneous transluminal coronary angioplasty (PTCA), peripheral artery disease (PAD) requiring percutaneous transluminal angioplasty (PTA), and acute stroke. Cumulative prescription days of ARBs beyond 365-760 days or more were found to be negatively correlated with incidence of MACEs. For patients with dual comorbidity (i.e., mellitus and hyperlipidemia), 91-365 cumulative prescription days might also attenuate the risk. Conclusions For patients on maintenance dialysis, the use of ARBs could significantly attenuate the risk of major cardiovascular events: AMI, acute stroke, and PAD requiring PTA.
Applying Cloud computing technologies to Gerontology and Geriatrics Health Care System (GGHCS)
(2013)
Hsu, Tse-Chuan
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Chang, Chih-Hung
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Chu, William C.
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Ho, Shinn-Ying
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Hsueh, Nien-Lin
At present, the cloud is now being acclaimed as a less expensive with low maintenance, entirely secure and compatible alternative to traditional computing networks used by physicians and healthcare organizations worldwide. These sensors will collect all the information that is required and the sensors will transmit it into the cloud. These sensors are always-on, real-time data compiling; it rules out manual collecting work and possibility of typing errors, and; it reposes the deployment process, as wireless networking signifies no need for cabling or other physical setup. As healthcare IT turns more widely used, and data storage and retrieval costs raise, more hospitals are anticipated to adopt cloud computing, encouraging the overall healthcare IT market with great potential impact on Gerontology and Geriatrics HealthCare System.
Association between Gastroenterological Malignancy and Diabetes Mellitus and Anti-Diabetic Therapy: A Nationwide, Population-Based Cohort Study
(2015-05-15)
Lin, Chien-Ming
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Huang, Hui-Ling
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Chu, Fang-Ying
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Fan, Hueng-Chuen
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Chen, Hung-An
Background The relationship between diabetes mellitus (DM) and cancer incidence has been evaluated in limited kinds of cancer. The effect of anti-diabetic therapy (ADT) on carcinogenesis among diabetic patients is also unclear. Materials and Methods Using population-based representative insurance claims data in Taiwan, 36,270 DM patients and 145,080 comparison subjects without DM were identified from claims from 2005 to 2010. The association between the top ten leading causes of cancer-related death in Taiwan and DM was evaluated. Whether ADT altered the risk of developing cancer was also investigated. Results Incidence of cancer at any site was significantly higher in patients with DM than in those without (p<0.001). The risk of carcinogenesis imparted by DM was greatest in gastroenterological malignancies (liver, pancreas, and colorectal cancer) as well as lung, breast and oral cancer (p<0.001). Among the oral types of ADT, metformin decreased the risk of lung and liver cancer, but had less effect on reducing the risk of colorectal cancer. alpha-glucosidase inhibitor decreased the risk of developing liver, colorectal, and breast cancer. Apart from intermediateacting insulin, rapid-acting, long-acting, and combination insulin treatment significantly reduced the overall cancer risk among all DM patients. In subgroup analysis, long-acting insulin treatment significantly decreased the risk of lung, liver, and colorectal cancer. Conclusion Our results supported the notion that pre-existing DM increases the incidence of gastroenterological cancer. ADT, especially metformin, alpha-glucosidase inhibitor, and long-acting insulin treatment, may protect patients with DM against these malignancies. It is crucial that oncologists should closely collaborate with endocrinologists to provide an optimal cancerspecific therapy and diabetic treatment to patients simultaneously with cancer and DM.
Bacterial alterations in salivary microbiota and their association in oral cancer
(2017-11-28)
Lee, Wei-Hsiang
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Chen, Hui-Mei
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Yang, Shun-Fa
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Liang, Chao
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Peng, Chih-Yu
Oral squamous cell carcinoma (OSCC) is the most common malignant neoplasm of the oral cavity and the fourth leading malignancy and cause of cancer-related death in the male population of Taiwan. Most cases are detected at advanced stages, resulting in poor prognosis. Therefore, improved detection of early oral health disorders is indispensable. The involvement of oral bacteria in inflammation and their association with OSCC progression provide a feasible target for diagnosis. Due to the nature of oral neoplasms, the diagnosis of epithelial precursor lesions is relatively easy compared with that of other types of cancer. However, the transition from an epithelial precursor lesion to cancer is slow and requires further and continuous follow-up. In this study, we investigated microbiota differences between normal individuals, epithelial precursor lesion patients, and cancer patients with different lifestyle habits, such as betel chewing and smoking, using next-generation sequencing. Overall, the oral microbiome compositions of five genera, Bacillus, Enterococcus, Parvimonas, Peptostreptococcus, and Slackia, revealed significant differences between epithelial precursor lesion and cancer patients and correlated with their classification into two clusters. These composition changes might have the potential to constitute a biomarker to help in monitoring the oral carcinogenesis transition from epithelial precursor lesion to cancer.
Boosting Evolutionary Support Vector Machine for Designing Tumor Classifiers from Microarray Data
(2007)
Huang, Hui-Ling
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Chen, Yi-Hsiung
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Koeberl, Dwight D.
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Ho, Shinn-Ying
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生物科技學系
Since there are multiple sets of relevant genes having the same high accuracy in fitting training data called model uncertainty, to identify a small set of informative genes from microarray data for designing an accurate tumor classifier for unknown samples is intractable. Support vector machine (SVM), a supervised machine learning technique, is one of the methods successfully applied to cancer diagnosis problems. This study proposes an SVM-based classifier with automatic feature selection associated with a boosting strategy. The proposed boosting evolutionary support vector machine (named BESVM) hybridizes the advantages of SVM, boosting using a majority-voting ensemble and an intelligent genetic algorithm for gene selection. The merits of the BESVM-based classifier are threefold: 1) a small set of used genes, 2) accurate test classification using leave-one-out cross-validation, and 3) robust performance by avoiding overfitting training data. Five benchmark datasets were used to evaluate the BESVM-based classifier. Simulation results reveal that BESVM performs well having a mean accuracy 94.26% using only 10.1 genes averagely, compared with the existing SVM and non-SVM based classifiers.
Bronchial asthma is associated with increased risk of chronic kidney disease
(2014-05-08)
Huang, Hui-Ling
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Ho, Shinn-Ying
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Li, Chien-Hsun
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Chu, Fang-Ying
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Ciou, Li-Ping
Background: Bronchial asthma influences some chronic diseases such as coronary heart disease, diabetes mellitus, and hypertension, but the impact of asthma on vital diseases such as chronic kidney disease is not yet verified. This study aims to clarify the association between bronchial asthma and the risk of developing chronic kidney disease. Methods: The National Health Research Institute provided a database of one million random subjects for the study. A random sample of 141 064 patients aged >= 18 years without a history of kidney disease was obtained from the database. Among them, there were 35 086 with bronchial asthma and 105 258 without asthma matched for sex and age for a ration of 1:3. After adjusting for confounding risk factors, a Cox proportional hazards model was used to compare the risk of developing chronic kidney disease during a three-year follow-up period. Results: Of the subjects with asthma, 2 196 (6.26%) developed chronic kidney disease compared to 4 120 (3.91%) of the control subjects. Cox proportional hazards regression analysis revealed that subjects with asthma were more likely to develop chronic kidney disease (hazard ratio [HR]: 1.56; 95% CI: 1.48-1.64; p < 0.001). After adjusting for sex, age, monthly income, urbanization level, geographic region, diabetes mellitus, hypertension, hyperlipidemia, and steroid use, the HR for asthma patients was 1.40 (95% CI: 1.33-1.48; p = 0.040). There was decreased HRs in steroid use (HR: 0.56; 95% CI: 0.62-0.61; p < 0.001) in the development of chronic kidney disease. Expectorants, bronchodilators, anti-muscarinic agents, airway smooth muscle relaxants, and leukotriene receptor antagonists may also be beneficial in attenuating the risk of chronic kidney disease. Conclusions: Patients with bronchial asthma may have increased risk of developing chronic kidney disease. The use of steroids or non-steroidal drugs in the treatment of asthma may attenuate this risk.
Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes
(2015-12-09)
Srinivasulu, Yerukala Sathipati
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Wang, Jyun-Rong
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Hsu, Kai-Ti
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Tsai, Ming-Ju
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Charoenkwan, Phasit
Background: Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results: This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions: The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.
Co-expression analysis identifies long noncoding RNA SNHG1 as a novel predictor for event-free survival in neuroblastoma
(2016-09-06)
Sahu, Divya
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Hsu, Chia-Lang
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Lin, Chen-Ching
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Yang, Tz-Wen
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Hsu, Wen-Ming
Despite of the discovery of protein therapeutic targets and advancement in multimodal therapy, the survival chance of high-risk neuroblastoma (NB) patients is still less than 50%. MYCN amplification is a potent driver of NB, which exerts its oncogenic activity through either activating or inhibiting the transcription of target genes. Recently, long noncoding RNAs (lncRNAs) are reported to be altered in cancers including NB. However, lncRNAs that are altered by MYCN amplification and associated with outcome in high-risk NB patients are limitedly discovered. Herein, we examined the expression profiles of lncRNAs and protein-coding genes between MYCN amplified and MYCN non-amplified NB from microarray (n = 47) and RNA-seq datasets (n = 493). We identified 6 lncRNAs in common that were differentially expressed (adjusted P = 0.05 and fold change = 2) and subsequently validated by RT-qPCR. The co-expression analysis reveals lncRNA, SNHG1 and coding gene, TAF1D highly co-expressed in NB. Kaplan-Meier analysis shows that higher expression of SNHG1 is significantly associated with poor patient survival. Importantly, multivariate analysis confirms high expression of SNHG1 as an independent prognostic marker for event-free survival (EFS) (HR = 1.58, P = 2.36E-02). In conclusion, our study unveils that SNHG1 is up-regulated by MYCN amplification and could be a potential prognostic biomarker for high-risk NB intervention.
Computational identification of ubiquitylation sites from protein sequences
(2008-07-15)
Tung, Chun-Wei
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Ho, Shinn-Ying
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生物科技學系
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生物資訊及系統生物研究所
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Department of Biological Science and Technology
Background: Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites. Results: We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, k-nearest neighbor, and NaiveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation. Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules. Conclusion: We have proposed an algorithm IPMA for mining informative physicochemical properties from protein sequences to build an SVM-based prediction system UbiPred. UbiPred can predict ubiquitylation sites accompanied with a prediction score each to help biologists in identifying promising sites for experimental verification. UbiPred has been implemented as a web server and is available at http://iclab.life.nctu.edu.tw/ubipred.
Design of accurate predictors for DNA-binding sites in proteins using hybrid SVM-PSSM method
(2007-07-01)
Ho, Shinn-Ying
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Yu, Fu-Chieh
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Chang, Chia-Yun
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Huang, Hui-Ling
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生物科技學系
In this paper, we investigate the design of accurate predictors for DNA-binding sites in proteins from amino acid sequences. As a result, we propose a hybrid method using support vector machine (SVM) in conjunction with evolutionary information of amino acid sequences in terms of their position-specific scoring matrices (PSSMs) for prediction of DNA-binding sites. Considering the numbers of binding and non-binding residues in proteins are significantly unequal, two additional weights as well as SVM parameters are analyzed and adopted to maximize net prediction (NP, an average of sensitivity and specificity) accuracy. To evaluate the generalization ability of the proposed method SVM-PSSM, a DNA-binding dataset PDC-59 consisting of 59 protein chains with low sequence identity on each other is additionally established. The SVM-based method using the same six-fold cross-validation procedure and PSSM features has NP = 80.15% for the training dataset PDNA-62 and NP = 69.54% for the test dataset PDC-59, which are much better than the existing neural network-based method by increasing the NP values for training and test accuracies up to 13.45% and 16.53%, respectively. Simulation results reveal that SVM-PSSM performs well in predicting DNA-binding sites of novel proteins from amino acid sequences. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
Designing predictors of halophilic and non-halophilic proteins using support vector machines
(2013)
Huang, Hui-Ling
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Charoenkwan, Phasit
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Srinivasulu, Yerukala Sathipati
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Lee, Hua-Chin
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Ho, Shinn-Ying
Finding the molecular features causes the halophilicity in the halostable organisms is helpful to understand the halophilic adaption. In this study, we proposed a prediction method for halophilic proteins by using a machine learning method. The stages of this study are six-fold. First, we establish a non-redundant dataset of the halophilic proteins, collected from NCBI, Uniprotkb and EMBL-EBI databases. The dataset consists of 245 positive and negative proteins with sequence identity < 25%. Second, the protein sequences are represented by three types of feature vector sets which include amino acid composition, dipeptide composition, and physicochemical properties. Third, we propose three classifiers based on support vector machine (SVM) to classify the halophilic proteins and non-halophilic proteins. Fourth, the independent test accuracies of the three efficient classifiers are larger than 83%. Fifth, an inheritable bi-objective combinatory genetic algorithm is utilized to select a set of 11 physicochemical properties (PCPs). Sixth, these abundant amino acids, high different dipeptides (amino acid pair) and 11 informative PCPs can support to analyze the halophilic and non-halophilic proteins.
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
(2015-02-21)
Huang, Hui-Ling
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Wu, Yu-Chung
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Su, Li-Jen
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Huang, Yun-Ju
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Charoenkwan, Phasit
Background: Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. Results: This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant-and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. Conclusions: The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.
EEG-based Motion Sickness Classification system with Genetic Feature Selection
(2013)
Ko, Li-Wei
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Lee, Hua-Chin
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Tsai, Shu-Fang
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Shih, Tsung-Chin
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Chuang, Ya-Ting
People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VIZ-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects.
Enterotype-based Analysis of Gut Microbiota along the Conventional Adenoma-Carcinoma Colorectal Cancer Pathway
(2019-07-29)
Yang, Tzu-Wei
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Lee, Wei-Hsiang
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Tu, Siang-Jyun
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Huang, Wei-Chih
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Chen, Hui-Mei
The dysbiosis of human gut microbiota is strongly associated with the development of colorectal cancer (CRC). The dysbiotic features of the transition from advanced polyp to early-stage CRC are largely unknown. We performed a 16S rRNA gene sequencing and enterotype-based gut microbiota analysis study. In addition to Bacteroides-and Prevotella-dominated enterotypes, we identified an Escherichia-dominated enterotype. We found that the dysbiotic features of CRC were dissimilar in overall samples and especially Escherichia-dominated enterotype. Besides a higher abundance of Fusobacterium, Enterococcus, and Aeromonas in all CRC faecal microbiota, we found that the most notable characteristic of CRC faecal microbiota was a decreased abundance of potential beneficial butyrate-producing bacteria. Notably, Oscillospira was depleted in the transition from advanced adenoma to stage 0 CRC, whereas Haemophilus was depleted in the transition from stage 0 to early-stage CRC. We further identified 7 different CAGs by analysing bacterial clusters. The abundance of microbiota in cluster 3 significantly increased in the CRC group, whereas that of cluster 5 decreased. The abundance of both cluster 5 and cluster 7 decreased in the Escherichia-dominated enterotype of the CRC group. We present the first enterotype-based faecal microbiota analysis. The gut microbiota of colorectal neoplasms can be influenced by its enterotype.
ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives
(2017-03-01)
Wang, Jyun-Rong
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Huang, Wen-Lin
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Tsai, Ming-Ju
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Hsu, Kai-Ti
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Huang, Hui-Ling
Motivation: Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. Results: We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods.