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ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization

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BMC BIOINFORMATICS 2008年第1期9卷 1-16页

作者： Huang, Wen-Lin Tung, Chun-Wei Ho, Shih-Wen Hwang, Shiow-Fen Ho, Shinn-Ying Natl Chiao Tung Univ Inst Bioinformat Hsinchu Taiwan Feng Chia Univ Inst Comp Sci & Informat Engn Taichung 40724 Taiwan Chin Min Inst Technol Dept Management Informat Syst Miaoli Taiwan Natl Chiao Tung Univ Dept Biol Sci & Technol Hsinchu Taiwan

Background: Gene Ontology (GO) annotation, which describes the function of genes and gene products across species, has recently been used to predict protein subcellular and subnuclear localization. Existing GO-based prediction methods for protein subcellular localization use the known accession numbers of query proteins to obtain their annotated GO terms. An accurate prediction method for predicting subcellular localization of novel proteins without known accession numbers, using only the input sequence, is worth developing. Results: This study proposes an efficient sequence-based method (named ProLoc-GO) by mining informative GO terms for predicting protein subcellular localization. For each protein, BLAST is used to obtain a homology with a known accession number to the protein for retrieving the GO annotation. A large number n of all annotated GO terms that have ever appeared are then obtained from a large set of training proteins. A novel genetic algorithm based method (named GOmining) combined with a classifier of support vector machine (SVM) is proposed to simultaneously identify a small number m out of the n GO terms as input features to SVM, where m << n. The m informative GO terms contain the essential GO terms annotating subcellular compartments such as GO: 0005634 (Nucleus), GO: 0005737 (Cytoplasm) and GO: 0005856 (Cytoskeleton). Two existing data sets SCL12 (human protein with 12 locations) and SCL16 (Eukaryotic proteins with 16 locations) with < 25% sequence identity are used to evaluate ProLoc-GO which has been implemented by using a single SVM classifier with the m = 44 and m = 60 informative GO terms, respectively. ProLoc-GO using input sequences yields test accuracies of 88.1% and 83.3% for SCL12 and SCL16, respectively, which are significantly better than the SVM-based methods, which achieve < 35% test accuracies using amino acid composition (AAC) with acid pairs and AAC with dipedtide composition. For comparison, ProLoc-GO using known accession nu

关键词： support vector machine Gene Ontology support vector machine classifier Ensemble classifier Matthews Correlation Coefficient

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Regional landscape mapping through a method of chain standardization of Landsat images

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LANDSCAPE AND URBAN PLANNING 2015年 134卷 1-9页

作者： Meng, Qingmin Mississippi State Univ Dept Geosci Starkville MS 39762 USA

Large area landscape analysis and modeling are often based on the data of land cover categories, which represent the interface between natural conditions and human impacts over time and across space. This study proposes a method of imagery chain standardization, which is straightforward and simple for landscape scientists to do large area land cover analysis with commonly available Landsat data. Unlike image normalization methods using complicated atmospheric correction and radiometric conservation, chain standardization proposes that for a large area, one Landsat image locating around the center of this area is used as the reference image. The reference image is first used to standardize its nearest neighboring images by applying a simple linear regression equation without intercept, which is developed based on the overlap imagery areas. Likewise, the firstly standardized images are used as new reference images to standardize their neighboring images further away from the study area center, and till all images covering a study area are transformed to the standard of the reference image. We test imagery chain standardization by using support vector machine classifier to classify the land cover of the states of Louisiana and Arkansas, USA, into agriculture, barren lands, forest, urban, water, and wetlands. 66-scene commonly available Landsat TM images are standardized, which results in promising mosaics and classification performance with overall accuracy values about 90% and Kappa coefficients close to 1. The imagery chain standardization is a promising method for landscape scientists to do regional landscape mapping and modeling. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Regional landscape analysis Land cover mapping Imagery chain standardization Linear regression support vector machine classifier

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Directional relays without voltage sensors for distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第12期8卷 2074-2082页

作者： Trung Dung Le Petit, Marc E3S SUPELEC Syst Sci Dept Power & Energy Syst F-91192 Gif Sur Yvette France

In this study, algorithms for directional relays using only current measurements are presented. Developed for radial distribution networks, these algorithms will determine fault direction based on ratios between variations of sequence currents during and before faults: Delta I-2/Delta I-0 ratio for line-to-ground faults and the Delta I-2/Delta I-1 ratio for line-to-line faults. The ratios are used as input of a support vector machine classifier, which was trained beforehand thanks to simulation tools. The classifier classifies ratios into two categories, according to the fault location: upstream or downstream towards the relay. Test results from simulations show good performances of the algorithms in most cases, with the presence of different distributed generation technologies. Moreover, impact of certain factors on algorithms, such as measurement errors, high-impedance faults or network reconfiguration, is studied. Finally, the implementation of algorithms is also discussed.

关键词： line-to-line faults support vector machine classifier relay protection voltage sensors network reconfiguration measurement errors power distribution faults distributed generation directional relays Current measurement Power system protection distributed power generation fault location electric current measurement current measurements high-impedance faults fault direction power distribution protection Distribution networks radial distribution networks support vector machines line-to-ground faults

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ON THE ANALYTICAL STUDY OF THE SERVICE QUALITY OF INDIAN RAILWAYS UNDER SOFT-COMPUTING PARADIGM

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TRANSPORT 2024年第1期39卷 54-63页

作者： Majumder, Saibal Singh, Aarti Singh, Anupama Karpenko, Mykola Sharma, Haresh Kumar Mukhopadhyay, Somnath Dr. BC Roy Engn Coll Dept Comp Sci & Engn Data Sci Durgapur India FORE Sch Management New Delhi India Birla Inst Management Technol Dept Strateg Environm Management Greater Noida India Vilnius Gediminas Tech Univ Dept Mobile Machinery & Railway Transport Vilnius Lithuania Birla Inst Management Technol Dept Operat Management & Decis Sci Greater Noida India Assam Univ Dept Comp Sci & Engn Silchar India

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 machine Learning (ML) model estimators: Extra Trees classifier (ETC), support vector machine classifier (SVMC) and Multinomial Naive Bayes classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

关键词： rough set theory extra trees classifier support vector machine classifier multinomial naive Bayes classifier performance metrics

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Bias in error estimation when using cross-validation for model selection

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BMC BIOINFORMATICS 2006年第1期7卷 1-8页

作者： Varma, S Simon, R NCI Biometr Res Branch Bethesda MD 20892 USA

Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. Results: We used CV to optimize the classification parameters for two kinds of classifiers;Shrunken Centroids and support vector machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the " null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of " null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM

关键词： support vector machine support vector machine classifier classifier Parameter True Error Inherent Bias

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Comparison of ICA approaches for facial expression recognition

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SIGNAL IMAGE AND VIDEO PROCESSING 2009年第4期3卷 345-361页

作者： Buciu, I. Kotropoulos, C. Pitas, I. Univ Oradea Fac Elect Engn & Informat Technol Dept Elect Oradea 410087 Romania Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

Independent component analysis (ICA) and Gabor wavelets extract the most discriminating features for facial action unit classification by employing either a cosine similarity measure (CSM) classifier or support vector machines (SVMs). So far, only the ICA approach, which is based on the InfoMax principle, has been tested for facial expression recognition. In this paper, in addition to the InfoMax approach, another five ICA approaches extract features from two facial expression databases. In particular, the Extended InfoMax ICA, the undercomplete ICA, and the nonlinear kernel-ICA approaches are exploited for facial expression representation for the first time. When applied to images, ICA treats the images as being mixtures of independent sources and decomposes them into an independent basis and the corresponding mixture coefficients. Two architectures for representing the images can be employed yielding either independent and sparse basis images or independent and sparse distributions of image representation coefficients. After feature extraction, facial expression classification is performed with the help of either a CSM classifier or an SVM classifier. A detailed comparative study is made with respect to the accuracy offered by each classifier. The correlation between the accuracy and the mutual information of independent components or the kurtosis is evaluated. Statistically significant correlations between the aforementioned quantities are identified. Several issues are addressed in the paper: (i) whether features having super-and sub-Gaussian distribution facilitate facial expression classification;(ii) whether a nonlinear mixture of independent sources improves the classification accuracy;and (iii) whether an increased "amount" of sparseness yields more accurate facial expression recognition. In addition, performance enhancements by employing leave-one-set of expressions-out and subspace selection are studied. Statistically significant differences in accuracy b

关键词： Independent component analysis Super-Gaussian distribution Sub-Gaussian distribution Nonlinear mixtures of independent sources Cosine similarity measure classifier support vector machine classifier Facial expression recognition Mutual information Kurtosis Correlation Statistical significance

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Sequence feature-based prediction of protein stability changes upon amino acid substitutions

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BMC GENOMICS 2010年第2期11卷 1-8页

作者： Teng, Shaolei Srivastava, Anand K. Wang, Liangjiang Clemson Univ Dept Genet & Biochem Clemson SC 29634 USA Greenwood Genet Ctr JC Self Res Inst Human Genet Greenwood SC 29646 USA

Background: Protein destabilization is a common mechanism by which amino acid substitutions cause human diseases. Although several machine learning methods have been reported for predicting protein stability changes upon amino acid substitutions, the previous studies did not utilize relevant sequence features representing biological knowledge for classifier construction. Results: In this study, a new machine learning method has been developed for sequence feature-based prediction of protein stability changes upon amino acid substitutions. support vector machines were trained with data from experimental studies on the free energy change of protein stability upon mutations. To construct accurate classifiers, twenty sequence features were examined for input vector encoding. It was shown that classifier performance varied significantly by using different sequence features. The most accurate classifier in this study was constructed using a combination of six sequence features. This classifier achieved an overall accuracy of 84.59% with 70.29% sensitivity and 90.98% specificity. Conclusions: Relevant sequence features can be used to accurately predict protein stability changes upon amino acid substitutions. Predictive results at this level of accuracy may provide useful information to distinguish between deleterious and tolerant alterations in disease candidate genes. To make the classifier accessible to the genetics research community, we have developed a new web server, called MuStab (http://***/mustab/).

关键词： support vector machine Protein Stability support vector machine classifier classifier Performance Matthews Correlation Coefficient

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A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

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HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 98-103页

作者： Tripathy, R. K. Sharma, L. N. Dandapat, S. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

关键词： MECG data matrix PMMSE cardiac disease classification cardiac dysrhythmia cardiovascular disease diagnostic feature vector diagnostic information diseases electrocardiography hypertrophy least square classifier medical diagnostic computing medical signal processing multilead electrocardiogram multivariate multiscale sample entropy myocardial infarction principal component principal component analysis support vector machine classifier support vector machines

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WiFi-based passive sensing system for human presence and activity event classification

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IET WIRELESS SENSOR SYSTEMS 2018年第6期8卷 276-283页

作者： Li, Wenda Tan, Bo Piechocki, Robert J. Univ Bristol Dept Elect & Elect Engn Bristol Avon England Coventry Univ Sch Comp Elect & Math Coventry W Midlands England

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

关键词： ubiquitous computing software defined radio system radiofrequency identification tags WiFi Doppler information analysis time-frequency domain feature vectors Signal processing and detection calibration Digital signal processing electronic devices activity event recognition Wireless sensor networks Knowledge engineering techniques Algebra support vector machine classifier noninvasive human sensing wireless LAN received signal strength approach Local area networks human presence detection e-healthcare wireless sensor networks Doppler effect vectors indoor context-awareness applications WiFi-based passive sensing system time-frequency analysis WiFi signal activity event classification Measurement standards and calibration support vector machines passive infrared approaches signal classification software radio

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Analysis of support vector machine and maximum likelihood classifiers in land cover classification using Sentinel-2 images

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PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022年第2期88卷 213-227页

作者： John, Susan Varghese, A. O. St Francis De Sales Coll Seminary Hills Nagpur 440006 Maharashtra India RRSC Cent Dept Space Amravati RdNRSC ISRO PB 003Univ Campus PO Nagpur 440033 Maharashtra India

Remote sensing data has been widely applied to classify the land cover more frequently and on a near real-time basis for updating as it is more economic, less time consuming compared to ground based survey. Accurate classification of the land use/cover classes such as water body, cropland, built-up area, scrub land, fallow land, forest etc., is one of the biggest challenges in natural resource inventory, management and monitoring. As accuracy of remote sensing data classification is affected by many parameters which include type of data, presence of heterogeneous landscapes in study area, classification approaches etc., as satellite imagery is complex in nature. Many classifiers have been developed and tested on remotely sensed data for better classification. Classification of remote sensing data is mainly divided into two categories such as supervised and unsupervised. In supervised classification, the decision boundaries in feature space are determined by training the samples. Two supervised classifiers namely maximum likelihood (ML) classifier, which is a parametric classifier that assumes data to be normally distributed, and support vector machine classifier (SVM) which is a non-parametric classifier are used in classification. In the present study, the accuracy of these two classifiers is studied on five different data sets of Sentinel-2 satellite image of different years and sessions to accommodate intra and inter annual variations of the datasets. Sentinel-2 satellite images covering part of Nagpur, located in Maharashtra, India were used for the classification. classifier accuracy has been calculated using overall accuracy and kappa statistics based on ground truth information. The result obtained were carefully examined by comparing classification accuracies and then by visual analysis. The result shows that SVM classifiers gives better overall accuracy and kappa coefficients and its average value for intra and inter annual classification outputs were 91.78

关键词： support vector machine classifier Maximum likelihood classifier Overall accuracy Kappa coefficient Sentinel-2

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