Browsing by Author "Lee, Hua-Chin"

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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.
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.
HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening
(2013-10-22)
Charoenkwan, Phasit
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Hwang, Eric
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Cutler, Robert W.
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Lee, Hua-Chin
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Ko, Li-Wei
Background: High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. Results: We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e. g., neurite length, and generic feature descriptors (GFDs), e. g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. Conclusions: Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.
Increased Risk of Major Depression in the Three Years following a Femoral Neck Fracture-A National Population-Based Follow-Up Study
(2014-03-13)
Chang, Chih-Yu
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Chen, Wen-Liang
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Liou, Yi-Fan
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Ke, Chih-Chi
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Lee, Hua-Chin
Femoral neck fracture is common in the elderly, and its impact has increased in aging societies. Comorbidities, poor levels of activity and pain may contribute to the development of depression, but these factors have not been well addressed. This study aims to investigate the frequency and risk of major depression after a femoral neck fracture using a nationwide population-based study. The Taiwan Longitudinal Health Insurance Database was used in this study. A total of 4,547 patients who were hospitalized for femoral neck fracture within 2003 to 2007 were recruited as a study group; 13,641 matched non-fracture participants were enrolled as a comparison group. Each patient was prospectively followed for 3 years to monitor the occurrence of major depression. Cox proportional-hazards models were used to compute the risk of major depression between members of the study and comparison group after adjusting for residence and sociodemographic characteristics. The most common physical comorbidities that were present after the fracture were also analyzed. The incidences of major depression were 1.2% (n = 55) and 0.7% (n = 95) in the study and comparison groups, respectively. The stratified Cox proportional analysis showed a covariate-adjusted hazard ratio of major depression among patients with femoral neck fracture that was 1.82 times greater (95% CI, 1.30-2.53) than that of the comparison group. Most major depressive episodes (34.5%) presented within the first 200 days following the fracture. In conclusion, patients with a femoral neck fracture are at an increased risk of subsequent major depression. Most importantly, major depressive episodes mainly occurred within the first 200 days following the fracture.
Increased risk of major depression subsequent to a first-attack and non-infection caused urticaria in adolescence: a nationwide population-based study
(2014-07-11)
Kuo, Chia-Lun
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Chen, Chi-Yen
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Huang, Hui-Ling
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Chen, Wen-Liang
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Lee, Hua-Chin
Background: Non-infection caused urticaria is a common ailment in adolescents. Its symptoms (e.g., unusual rash appearance, limitation of daily activities, and recurrent itching) may contribute to the development of depressive stress in adolescents; the potential link has not been well studied. This study aimed to investigate the risk of major depression after a first-attack and non-infection caused urticaria. Methods: This study used the Taiwan Longitudinal Health Insurance Database. A total of 5,755 adolescents hospitalized for a first-attack and non-infection caused urticaria from 2005 to 2009 were recruited as the study group, together with 17,265 matched non-urticarial enrollees who comprised the control group. Patients who had any history of urticaria or depression prior to the evaluation period were excluded. Each patient was followed for one year to identify the occurrence of depression. Cox proportional hazards models were generated to compute the risk of major depression, adjusting for the subjects\' sociodemographic characteristics. Depression-free survival curves were also analyzed. Results: Thirty-four (0.6%) adolescents with non-infection caused urticaria and 59 (0.3%) non-urticarial control subjects suffered a new-onset episode of major depression during the study period. The stratified Cox proportional analysis showed that the crude hazard ratio (HR) of depression among adolescents with urticaria was 1.73 times (95% CI, 1.13-2.64) than that of the control subjects without urticaria. Moreover, the HR were higher in physical (HR: 3.39, 95% CI 2.77-11.52) and allergy chronic urticaria (HR: 2.43, 95% CI 3.18-9.78). Conclusion: Individuals who have a non-infection caused urticaria during adolescence are at a higher risk of developing major depression.
Is schizophrenia associated with an increased risk of chronic kidney disease? A nationwide matched-cohort study
(2015-01-01)
Tzeng, Nian-Sheng
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Hsu, Yung-Ho
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Ho, Shinn-Ying
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Kuo, Yu-Ching
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Lee, Hua-Chin
Objective: The impact of schizophrenia on vital diseases, such as chronic kidney disease (CKD), has not as yet been verified. This study aims to establish whether there is an association between schizophrenia and CKD. Design: A nationwide matched cohort study. Setting: Taiwan's National Health Insurance Research Database. Participants: A total of 2338 patients with schizophrenia, and 7014 controls without schizophrenia (1: 3), matched cohort for sex, age group, geography, urbanisation and monthly income, between 1 January 2003 and 31 December 2007, based on the International Classifications of Disease Ninth Edition (ICD-9), Clinical Modification codes. Primary and secondary outcome measures: After making adjustments for confounding risk factors, a Cox proportional hazards model was used to compare the risk of developing CKD during a 3-year follow-up period from the index date. Results: Of the 2338-subject case cohort, 163 (6.97%) developed a CKD, as did 365 (5.20%) of the 7014 control participants. Cox proportional hazards regression analysis revealed that patients with schizophrenia were more likely to develop CKD (HR = 1.36, 95% CI 1.13 to 1.63; p < 0.001). After adjusting for gender, age group, hypertension, diabetes mellitus, hyperlipidaemia, heart disease and nonsteroid anti-inflammatory drugs (NSAIDs) usage, the HR for patients with schizophrenia was 1.25 (95% CI 1.04 to 1.50; p < 0.05). Neither typical nor atypical antipsychotics was associated an increased risk of CKD in patients with schizophrenia. Conclusions: The findings from this population-based retrospective cohort study suggest that schizophrenia is associated with a 25% increase in the risk of developing CKD within only a 3-year follow-up period.
Is Zolpidem Associated with Increased Risk of Fractures in the Elderly with Sleep Disorders? A Nationwide Case Cross-Over Study in Taiwan
(2015-12-30)
Tang, Yih-Jing
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Ho, Shinn-Ying
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Chu, Fang-Ying
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Chen, Hung-An
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Yin, Yun-Ju
Background We conducted a study using a case-crossover design to clarify the risk of acute effects of zolpidem and benzodiazepine on all-sites of fractures in the elderly. Design of study Case-crossover design. Methods and Materials Elderly enrollees (n = 6010) in Taiwan's National Health Insurance Research Database with zolpidem or benzodiazepine use were analyzed for the risk of developing fractures. Results After adjusting for medications such as antipsychotics, antidepressants, and diuretics, or comorbidities such as hypertension, osteoarthritis, osteoporosis, rheumatoid arthritis and depression, neither zolpidem nor benzodiazepine was found to be associated with increased risk in all-sites fractures. Subjects without depression were found to have an increased risk of fractures. Diazepam is the only benzodiazepine with increased risk of fractures after adjusting for medications and comorbidities. Hip and spine were particular sites for increased fracture risk, but following adjustment for comorbidities, the associations were found to be insignificant. Conclusion Neither zolpidem nor benzodiazepine was associated with increased risk of all-site fractures in this case cross-over study after adjusting for medications or comorbidities in elderly individuals with insomnia. Clinicians should balance the benefits and risks for prescribing zolpidem or benzodiazepine in the elderly accordingly.
Motion Sickness Estimation System
(2012)
Lin, Chin-Teng
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Tsai, Shu-Fang
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Lee, Hua-Chin
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Huang, Hui-Lin
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Ho, Shinn-Ying
Motion sickness occurs when the brain receives conflicting sensory information from body, inner ear and eyes [1]. In some cases, a decreased ability to actively control the body's postural motion also causes motion sickness [2][3]. Many previous studies have indicated that motion sickness had negative effect on driving performance, and sometimes lead to serious traffic accidents due to self-control ability decline. Therefore motion sickness becomes a very important issue in our daily life especially considering driving safety. There are many attempts made by researchers to realize motion sickness, and detect motion sickness in the early stage. Although many motion-sickness-related biomarkers have been identified, estimating human motion sickness level (MSL) remains a challenge in operational environment. In our past studies, we found that features in the occipital area were highly correlated with the driver's driving performance. In this study, we designed a virtual-reality (VR) based driving environment with instinct-MSL-reporting mechanism. When a subject performed a driving task, his/her brain EEG was recorded simultaneously. From those EEG data, features associated with left motor brain area, parietal brain area and occipital midline brain area which predicted MSL were extracted by an optimal classifier implemented by an inheritable bi-objective combinatorial genetic algorithm (IBCGA) with support vector machine. Unlike traditional correlation-based method, IBCGA aims to select a small set of EEG features and maximize the prediction accuracy simultaneously in BCI applications. Once the optimal feature set predicting MSL is successfully found, a driver's cognitive state can be monitored.
Predicting Protein Crystallization Using a Simple Scoring Card Method
(2013)
Shoombuatong, Watshara
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Charoenkwan, Phasit
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Huang, Hui-Ling
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Lee, Hua-Chin
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Chaijaruwanich, Jeerayut
Many computational methods have been developed to predict protein crystallization. Most methods use amino acid and dipeptide compositions as part of the informative features. To advance the prediction accuracy, the support vector machine (SVM) based classifiers and ensemble approaches were effective and commonly-used techniques. However, these techniques suffer from the low interpretation ability of insight into crystallization. In this study, we utilize a newly-developed scoring card method (SCM) with a dipeptide composition feature to predict protein crystallization. This SCM classifier obtains prediction results 74%, 0.55 and 0.83 for accuracy, sensitivity and specificity, respectively, which is comparable to the SVM classifier using the same benchmarks. The experimental results show that the SCM classifier has advantages of simplicity, high interpretability, and high accuracy in predicting protein crystallization, compared with existing SVM-based ensemble classifiers.
Predicting Television Ratings and Its Application to Taiwan Cable TV Channels
(2013)
Huang, Hui-Ling
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Lee, Hua-Chin
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Shu, Li-Sun
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Lai, Shih-Chung
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Tsai, Tse-Ming
Using forecast television network ratings, television executives estimate a price to sell time to advertisers. TV rating is an important feedback mechanism because its results greatly affect the immense profits of TV companies, advertisers, and program producers. Therefore, how to select the samples for TV rating investigation plays an important role in predicting program ratings. How to design an accurate predicting model for program rating also is an important investigation. The predicting problem is essentially a bi-objective optimization problem which minimizes the number of samples and maximizes the predicting accuracy of program rating. In this study, we propose an evolutionary approach to designing a rating model (ERM) by simultaneous optimization of sampling sub-area selection and parameter tuning using an intelligent genetic algorithm (IGA). In this study, the ERM is applied to Taiwan Cable TV Channels in Taipei and Taiwan. The experiments show that TV rating prediction of the proposed ERM is efficient smaller than that of using the same number of sub-areas with the largest TV ratings and an optimal prediction program rating by using the selected sub-areas.
Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition
(2012-12-13)
Huang, Hui-Ling
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Charoenkwan, Phasit
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Kao, Te-Fen
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Lee, Hua-Chin
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Chang, Fang-Lin
Background: Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. Results: This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of a-helix structure and thermophilic proteins to be soluble. Conclusions: The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. Availability: The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.
Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition
(2012-12-13)
Huang, Hui-Ling
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Charoenkwan, Phasit
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Kao, Te-Fen
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Lee, Hua-Chin
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Chang, Fang-Lin
Background: Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. Results: This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of a-helix structure and thermophilic proteins to be soluble. Conclusions: The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. Availability: The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.
Prediction of Mouse Senescence from HE-Stain Liver Images Using an Ensemble SVM Classifier
(2013)
Huang, Hui-Ling
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Hsu, Ming-Hsin
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Lee, Hua-Chin
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Charoenkwan, Phasit
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Ho, Shinn-Jang
Study of cellular senescence from images in molecular level plays an important role in understanding the molecular basis of ageing. It is desirable to know the morphological variation between young and senescent cells. This study proposes an ensemble support vector machine (SVM) based classifier with a novel set of image features to predict mouse senescence from HE-stain liver images categorized into four classes. For the across-subject prediction that all images of the same mouse are divided into training and test images, the test accuracy is as high as 97.01% by selecting an optimal set of informative image features using an intelligent genetic algorithm. For the leave-one-subject-out prediction that the test mouse is not involved in the training images of 20 mice, we identified eight informative feature sets and established eight SVM classifiers with a single feature set. The best accuracy of using an SVM classifier is 71.73% and the ensemble classifier consisting of these eight SVM classifiers can advance performance with accuracy of 80.95%. The best two feature sets are the gray level correlation matrix for describing texture and Haralick texture set, which are good morphological features in studying cellular senescence.
SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs
(2013-09-03)
Charoenkwan, Phasit
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Shoombuatong, Watshara
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Lee, Hua-Chin
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Chaijaruwanich, Jeerayut
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Huang, Hui-Ling
Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p = 0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.
SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides
(2014-12-08)
Liou, Yi-Fan
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Charoenkwan, Phasit
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Srinivasulu, Yerukala Sathipati
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Vasylenko, Tamara
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Lai, Shih-Chung
Background: Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed. Results: This work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO: 0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible. Conclusions: SCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.
Statistical Analysis and Classification of EEG-based Attention Network Task Using Optimized Feature Selection
(2014)
Lee, Hua-Chin
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Ko, Li-Wei
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Huang, Hui-Ling
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Wu, Jui-Yun
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Chuang, Ya-Ting
This research incorporates optimized feature selection using an inheritable bi-objective combinatorial genetic algorithm (IBCGA) and mathematic modeling for classification and analysis of electroencephalography (EEG) based attention network. It consists of two parts. 1) We first design the attention network experiments, record the EEG signals of subjects from NeuronScan instrument, and filter noise from the EEG data. We use alerting scores, orienting scores, and conflict scores to serve as the efficiency evaluation of the attention network. 2) Based on an intelligent evolutionary algorithm as the core technique, we analyze the large-scale EEG data, identify a set of important frequency-channel factors, and establish mathematical models for within-subject, across-subject and leave-one-subject-out evaluation using a global optimization approach. The results of using 10 subjects show that the average classification accuracy of independent test in the within-subject case is 86.51%, the accuracy of the across-subject case is 68.44%, and the accuracy of the leave-one-subject-out case is 54.33%