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Prediction of anti-inflammatory proteins/peptides: an insilico approach

JOURNAL OF TRANSLATIONAL MEDICINE 2017年第1期15卷 1-11页

作者： Gupta, Sudheer Sharma, Ashok K. Shastri, Vibhuti Madhu, Midhun K. Sharma, Vineet K. Indian Inst Sci Educ & Res Bhopal Dept Biol Sci Metagen & Syst Biol Grp Bhopal India

Background: The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer's disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics. Methods: In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. The training was performed using experimentally validated epitopes obtained from Immune epitope database and analysis resource database. Different sequence-based features and their hybrids with motif information were employed for development of support vector machine-based machine learning models. Similarly, machine learning models were also constructed using random forest. Results: The composition and terminal residue conservation analysis of peptides revealed the dominance of leucine, serine, tyrosine and arginine residues in anti-inflammatory epitopes as compared to non anti-inflammatory epitopes. Similarly, the anti-inflammatory epitopes specific motifs were found to be rich in hydrophobic and polar residues. The hybrid of tripeptide composition-based support vector machine model and motif yielded the best performance on 10-fold cross validation with an accuracy of 78.1% and MCC of 0.58. The same displayed an accuracy of 72% and MCC of 0.45 on validation dataset, rejecting any possibility of over-fitting. The tripeptide composition-based random forest model displayed an accuracy of 0.8 and MCC of 0.59 on 10-fold cross validation, however, the accuracy (0.68) and MCC (0.3

关键词： support vector machine Random Forest Dipeptide Area Under Curve support vector machine model

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Advanced method for short-term wind power prediction with multiple observation points using extreme learning machines

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JOURNAL OF ENGINEERING-JOE 2018年第1期2018卷 29-38页

作者： Mahmoud, Tawfek Dong, Zhao Yang Ma, Jin Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ NSW Sch Elect Engn & Telecommun Sydney NSW Australia

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power-speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.

关键词： power engineering computing grey systems mean square error methods support vector machines fuzzy reasoning fuzzy neural nets time series learning (artificial intelligence) wind power plants mean absolute error Grey predictor model wind power estimation root mean square error short-term wind power prediction artificial intelligence technique high-quality wind power prediction power system planning power system operation power system control advanced time series processing method extreme learning machine technique historical records numerical weather information real wind farm sites Australia region wind speed capacity factor wind power-speed curve artificial neural network fuzzy logic model adaptive neuro-fuzzy inference system support vector machine model model verification real wind measurements weather station Australia

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Construction of precise support vector machine based models for predicting promoter strength

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Frontiers of Electrical and Electronic Engineering in China 2017年第1期5卷 90-98页

作者： Hailin Meng Yingfei Ma Guoqin Mai Yong Wang Chenli Liu Bioengineering Research Center Guangzhou Institute of Advanced Technology Chinese Academy of Sciences Guangzhou 511458 China Center for Synthetic Biology Engineering Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Chinese Academy of Sciences Key Laboratory of Synthetic Biology Institute of Plant Physiology and Ecology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 200032 China

Background： The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the appfied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network （ANN） has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine （SVM） is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem. Methods： To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set （≥10 sequences） for model training and a test set （≥ 10 sequences） for model test. Results： The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set （R^2〉 0.99） and the test set （R^2〉 0.98）, both of which are better than that of ANN obtained by our previous work. Conclusions： Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.

关键词： support vector machine model quantitative prediction promoter strength machine learning

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Application and Performance Evaluation of Object Detection Technology Based on Computer Vision 14

Application and Performance Evaluation of Object Detection T...

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14th Asian Control Conference (ASCC)

作者： Li, WeiXue Zhang, Hang Wan, Cong Shi, JiaWen Li, TingRu Natl Key Lab Complex Syst Control & Intelligent A Beijing Peoples R China Minist Ind & Informat Technol Talent Exchange Ctr Beijing Peoples R China

ISBN: (纸本)9798331540845;9789887581598

In light of the challenges posed by the variability of industrial part shapes, weak texture information, and surface reflections in current industrial vision inspection, traditional methods such as feature descriptors and template matching are inadequate. This paper innovatively proposes a machine learning object detection method, establishes robust stable features for weakly textured industrial parts, and evaluates the performance of the model and feature selection in combination with actual fuel tank cap detection tasks. Firstly, we construct a dataset of fuel tank cap targets and establish a comprehensive and relatively stable feature dataset, including geometric features, texture features, and color features. Secondly, through ablation experiments and comparative experiments, we conduct intelligent performance testing on model detection accuracy, parameter selection, and feature contribution. Finally, the evaluation results demonstrate that the support vector machine (SVM) model exhibits the best detection performance after feature selection, with a detection accuracy of 99.0% under the optimal parameter settings validated through experiments.

关键词： Industrial Vision Inspection Intelligent Performance Testing Ablation Experiments support vector machine model

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support vector machine Predictive Control for Superheated Steam Temperature Based on Particle Swarm Optimization

Support Vector Machine Predictive Control for Superheated St...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhao, Dandan Liang, Ping South China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781424448135

The processes with characters of nonlinear and time-varying is common in power plant. In order to achieve high control performance and effectiveness, a predictive control strategy combining support vector machine model and particle swarm optimization algorithm is proposed. Simulation of the superheated steam temperature system was done in a 600MW supercritical once-through boiler. Experimental results show good control performance in terms of reference command tracking ability and steady state errors. This method is better than PID control in robustness and dynamic performance.

关键词： support vector machine model particle swarm optimization predictive control superheated steam temperature

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Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

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BMC Research Notes 2014年第1期7卷 1-11页

作者： Kayvanjoo, Amir Hossein Ebrahimi, Mansour Haqshenas, Gholamreza Department of Biology School of Basic Sciences University of Qom Qom Iran Microbiology Department Monash University Melbourne Australia

Background: Hepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence.;Results: Using combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups.;Conclusions: The present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection. © © 2014KayvanJoo et al.;licensee BioMed Central Ltd.

关键词： support vector machine Sustained Virological Response support vector machine model Boceprevir Linear support vector machine

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Coreference based event-argument relation extraction on biomedical text

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Journal of Biomedical Semantics 2011年第5期2卷 1-14页

作者： Yoshikawa, Katsumasa Riedel, Sebastian Hirao, Tsutomu Asahara, Masayuki Matsumoto, Yuji Nara Institute of Science and Technology Graduate School of Information Science Ikoma Nara 8916-5 Takayama Japan University of Massachusetts Amherst Amherst 01002 MA United States NTT Communication Science Laboratories 2-4 Hikaridai Seika-cho Keihanna Science City Kyoto Japan

This paper presents a new approach to exploit coreference information for extracting event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations based on the concept of salience in discourse;(2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on support vector machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information. © 2011 Yoshikawa et al;licensee BioMed Central Ltd.

关键词： support vector machine support vector machine model Joint model Event Extraction Relation Extraction

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Feature selection and validated predictive performance in the domain of Legionella pneumophila: A comparative study

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BMC Research Notes 2016年第1期9卷 1-7页

作者： Van Der Ploeg, Tjeerd Steyerberg, Ewout W. Department of Science Medical Center Alkmaar Inholland University Alkmaar Netherlands Department of Public Health Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands

Background: Genetic comparisons of clinical and environmental Legionella strains form an essential part of outbreak investigations. DNA microarrays often comprise many DNA markers (features). Feature selection and the development of prediction models are particularly challenging in this domain with many variables and comparatively few subjects or data points. We aimed to compare modeling strategies to develop prediction models for classifying infections as clinical or environmental. Methods: We applied a bootstrap strategy for preselecting important features to a database containing 222 Legionella pneumophila strains with 448 continuous markers and a dichotomous outcome (clinical or environmental). Feature selection was done with 50 bootstrap samples resulting in a top 10 of most important features for each of four modeling techniques: classification and regression trees (CART), random forests (RF), support vector machines (SVM) and least absolute shrinkage and selection operator (LASSO). Validation was done in a second bootstrap resampling loop (200x) for evaluation of discriminatory model performance according to the AUC. Results: The top 5 of selected features differed considerably between the various modeling techniques, with only one common feature ("LePn.007B8"). The mean validated AUC-values of the SVM model and the CART model were 0.859 and 0.873 respectively. The LASSO and the RF model showed higher validated AUC-values (0.925 and 0.975 respectively). Conclusions: In the domain of Legionella pneumophila, which comprises many potential features for classifying of infections as clinical or environmental, the RF and LASSO techniques provide good prediction models. The identification of potentially biologically relevant features is highly dependent on the technique used, and should hence be interpreted with caution. © 2016 van de Ploeg and Steyerberg.

关键词： support vector machine Feature Selection Random Forest support vector machine model Little Absolute Shrinkage Selection Operator

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Reliability prediction of CNC machine tools based on GM(1,1)-SVM combined model 5

Reliability prediction of CNC machine tools based on GM(1,1)...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Li, Dong-yang Piao, Cheng-dao Wang, De-chao Zhao, De-jin Yanbian Univ Engn Coll Yanji Jilin Peoples R China

ISBN: (纸本)9781665423144

Aiming at the characteristics of low cost and complex influencing factors of CNC machine tool failure data sample collection, a combination prediction model based on gray prediction and support vector machine is proposed, use Python and MATLAB software to predict the time between failures of CNC machine tools in the SVM model and the GM(1,1) model, respectively, and obtain the predicted time between failures, use the standard deviation method to determine the weight of the combined model, and establish GM(1,1)-SVM combined prediction model, the accuracy and practicability of the combined forecasting model are verified through examples. The results show that the prediction of the combined model is higher than that of the single model and the results are reliable, the combined model provides a theoretical basis for the CNC machine tool users and maintenance personnel to maintain it in advance, and has guiding value for the reliability prediction.

关键词： Grey model support vector machine model GM (1,1)-SVM combined model Reliability prediction

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Analysis and prediction of cancerlectins using evolutionary and domain information

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BMC Research Notes 2011年第1期4卷 1-9页

作者： Kumar, Ravi Panwar, Bharat Chauhan, Jagat S. Raghava, Gajendra Ps Bioinformatics Centre Institute of Microbial Technology Chandigarh India

Background: Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins. Results: All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring

关键词： support vector machine Amino Acid Composition support vector machine model Radial Basis Function Kernel Evolutionary Information

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