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Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding

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BMC BIOINFORMATICS 2016年第1期17卷 1-11页

作者： Huang, Yu-An You, Zhu-Hong Chen, Xing Chan, Keith Luo, Xin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon 999077 Hong Kong Peoples R China

Background: Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information. Results: In this study, we present a novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence. Two kinds of protein descriptors, composition and transition, are extracted for representing each protein sequence. On the basis of such a feature representation, novel weighted sparse representation based classifier is introduced to predict protein interaction class. When the proposed method was evaluated with the PPIs data of S. cerevisiae, Human and H. pylori, it achieved high prediction accuracies of 96.82, 97.66 and 92.83 % respectively. Extensive experiments were performed for cross-species PPIs prediction and the prediction accuracies were also very promising. Conclusions: To further evaluate the performance of the proposed method, we then compared its performance with the method based on support vector machine (SVM). The results show that the proposed method achieved a significant improvement. Thus, the proposed method is a very efficient method to predict PPIs and may be a usefu

关键词： Receiver Operator Characteristic Curve Local Binary Pattern support vector machine classifier Protein Pair Feature Extraction Method

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Research on biomechanical compatibility for the artificial anal sphincter based on rectal perception function reconstruction

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2015年第8期9卷 921-927页

作者： Zan, Peng Zhao, Jie Yang, Lihong Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Shanghai 200072 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

To treat the anal incontinence, a new artificial anal sphincter is put forward. Aiming at the rectal perception function reconstruction and the mechanical compatibility for artificial anal sphincter, this paper regards the huge mobile contraction wave in the typical rectal pressure contraction waves as the main signal of defecation. Meanwhile, by extracting features of rectal pressure signals with the help of wavelet packet analysis, and by adopting the Davies Bouldin index (DB) optimal algorithm, rectal perception function reconstruction is realised through predicting the defecation by means of the SVM classifier. In order to avoid the rectal ischemia necrosis, a three-dimensional finite element model for the rectum and artificial anal sphincter is constructed, with the purpose of analysing the viscoelasticity of the rectum. Then, prony series is deduced by utilising the constitutive equation of rectum, and finite element analysis for artificial anal sphincter is conducted by simulating the contraction and relaxation of the sphincter to generate load. At last, the shift and the stress distribution for the rectum at the time of being oppressed are obtained. This simulative experiment result shows that the artificial anal sphincter can highly accurately predict the defecation and it features favorable biomechanical compatibility.

关键词： biomechanics biological organs support vector machines feature extraction finite element analysis prosthetics viscoelasticity biomechanical compatibility artificial anal sphincter rectal perception function reconstruction feature extraction wavelet packet analysis DB optimal algorithm support vector machine classifier three-dimensional finite-element model rectum viscoelasticity Prony series stress distribution anal incontinence

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Higher order statistics for modulation and STBC recognition in MIMO systems

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IET COMMUNICATIONS 2019年第16期13卷 2436-2446页

作者： Khosraviyani, Mokhtar Kalbkhani, Hashem Shayesteh, Mahrokh G. Urmia Univ Dept Elect Engn Orumiyeh Iran Urmia Univ Technol Fac Elect Engn Orumiyeh Iran Sharif Univ Technol Elec Engn Dept ACRI Wireless Res Lab Tehran Iran

Identification of modulation and space-time block code (STBC) is an important task of receivers in applications such as military, civilian, and commercial communications. Here, we consider multiple-input multiple-output (MIMO) systems. We propose two methods for STBC identification when the modulation is known. We also introduce a method for joint identification of code and modulation. Additionally, we present an enhanced zero-forcing (ZF) equaliser to improve the separation between the features of different classes. Higher order cumulants are used as the statistical features. In the first method of STBC identification, after the proposed equalisation, received data samples are segmented, and then using the mean and Frobenius norm of the covariance matrix of extracted cumulants as threshold values, the STBCs are detected. This method requires the knowledge of the threshold values for each modulation type and noise power. In the second method, the knowledge of noise power and the type of modulation are not needed. After the proposed ZF equalisation, feature vector based on the cumulants is calculated, and then support vector machine (SVM) classifier is used to classify different STBCs. In the third method, joint detection of STBC and modulation type is performed using the proposed ZF equaliser and mapping the received samples. This method deals with the theoretical values calculated from the cumulants of four STBCs and four modulations, where there is no need to know the noise power. The results indicate that the proposed methods perform well even at low signal-to-noise ratios (SNRs).

关键词： equalisers higher order statistics support vector machines covariance matrices MIMO communication space-time block codes pattern classification telecommunication computing higher order statistics STBC recognition MIMO systems space-time block code multiple-input multiple-output systems STBC identification joint identification higher order cumulants statistical features threshold values modulation type noise power feature vector joint detection ZF equaliser Frobenius norm support vector machine classifier zero-forcing equaliser covariance matrix SVM classifier signal-to-noise ratios

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Real-time embedded implementation of robust speed-limit sign recognition using a novel centroid-to-contour description method

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IET COMPUTER VISION 2017年第6期11卷 407-414页

作者： Tsai, Chi-Yi Liao, Hsien-Chen Hsu, Kuang-Jui Tamkang Univ Dept Elect & Comp Engn 151 Ying Chuan Rd New Taipei 251 Taiwan

Traffic sign recognition is a very important function in automatic driving assistance systems (ADAS). This study addresses the design and implementation of a vision-based ADAS based on an image-based speed-limit sign (SLS) recognition algorithm, which can automatically detect and recognise SLS on the road in real-time. To improve the recognition rate of SLS having different orientations and scales in the image, this study also presents a new sign content description algorithm, which describes the detected road sign using centroid-to-contour (CtC) distances of the extracted sign content. The proposed CtC descriptor is robust to translation, rotation and scale changes of the SLS in the image. This advantage improves the recognition accuracy of a support vector machine classifier trained using a large database of traffic signs. The proposed SLS recognition method had been implemented on two different embedded platforms, each of them equipped with an ARM-based Quad-Core CPU running Android 4.4 operating system. Experimental results validate that the proposed method not only provides a high recognition rate, but also achieves real-time performance up to 30 frames per second for processing 1280x720 video streams running on a commercial ARM-based smartphone.

关键词： support vector machines image classification learning (artificial intelligence) embedded systems Android (operating system) microprocessor chips video streaming smart phones real-time embedded implementation centroid-to-contour description method traffic sign recognition automatic driving assistance system vision-based ADAS image-based speed-limit sign recognition algorithm SLS recognition algorithm sign content description algorithm CtC description method sign content extraction support vector machine classifier training ARM-based quadcore CPU Android 4 4 operating system ARM-based smartphone video streaming

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Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines

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BMC BIOINFORMATICS 2009年第1期10卷 1-15页

作者： Guan, Wei Zhou, Manshui Hampton, Christina Y. Benigno, Benedict B. Walker, DeEtte Gray, Alexander McDonald, John F. Fernandez, Facundo M. Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Georgia Inst Technol Sch Chem & Biochem Atlanta GA 30332 USA Ovarian Canc Inst Atlanta GA 30332 USA Georgia Inst Technol Sch Biol Atlanta GA 30332 USA

Background: The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease. Results: In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/ time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM. Conclusion: Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.

关键词： support vector machine Feature Selection Prediction Performance support vector machine classifier Feature Subset

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AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration

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IEEE ACCESS 2020年 8卷 149077-149092页

作者： Khan, Baber Ali, Ahmad Jalil, Abdul Mehmood, Khizer Murad, Maria Awan, Hamdan Int Islamic Univ Islamabad Dept Elect Engn Islamabad 44000 Pakistan Bahria Univ Dept Software Engn Islamabad 44000 Pakistan WIT Telecommun Syst & Software Res Grp TSSG Waterford X91 WR86 Ireland

During recent years correlation tracking is considered fast and effective by the virtue of circulant structure of the sampling data for learning phase of filter and Fourier domain calculation of correlation. During the occurrence of occlusion, motion blur and out of view movement of target, most of the correlation filter based trackers start to learn using erroneous samples and tracker starts drifting. Currently, adaptive correlation filter based tracking algorithms are being combined with redetection modules. This hybridization helps in redetection of the target in long term tracking. The redetection modules are mostly classifier, which classify the true object after tracking failure occurrence. The methods perform favorable during short term occlusion or partial occlusion. To further increase the tracking efficiency specifically during long term occlusion, while maintaining real time processing speed, this study proposes tracking failure avoidance method. We first propose, a strategy to detect the occlusion using two cues from the response map i.e., peak correlation score and peak to side lobe ratio. After successful detection of tracking failure, second strategy is proposed to save the target being getting more erroneous. Kalman filter based predictor continuously predicts the location during occlusion. Kalman filter passes this result to support vector machine (SVM). When the target reappears in frame, support vector machine based classifier classifies the correct object using the predicted location of Kalman filter. This decreases the chance of tracking failure as Kalman filter continuously updates itself during occlusion and predicts the next location using its own previous prediction. Once the true object is detected by classifier after the clearance of occlusion, this result is forwarded to correlation filter tracker to resume its operation of tracking and updating its parameters. Together these two proposed schemes show significant improvement in tracking e

关键词： Correlation Target tracking Filtering algorithms Estimation Information filters Adaptive correlation filter Kalman filter occlusion support vector machine classifier

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Tablet identification using support vector machine based text recognition and error correction by enhanced n-grams algorithm

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IET IMAGE PROCESSING 2020年第7期14卷 1366-1372页

作者： Dhivya, A. B. Sundaresan, M. Bharathiar Univ Dept Informat Technol Coimbatore Tamil Nadu India

Unidentified and/or misidentified tablets always present challenges to both patients and health care professionals alike. Consumption of these misidentified tablets often results in adverse drug reaction and sometimes may even cause ill health leading to death. Thus, identification of unknown tablets is an important task in the medical industry. This study proposes an algorithm that uses the text imprinted on the tablet images to identify unknown images. Text imprinted on pills often contains important information that can be used to identify tablets. In this study, multiple feature sets were extracted from tablet images. The proposed work first identifies the text region, from which the text is recognised using support vector machine classifier. A post processing algorithm based on the confusion model combined with n-grams is used to correct the substitution, insertion and deletion errors in the text recognised. Finally, the corrected text is matched with a template tablet database to identify similar tablets. Experimental results showed that the proposed system is efficient with respect to accuracy and can be safely used by both common public and health care professionals to identify tablets.

关键词： support vector machines health care drugs text analysis feature extraction tablet identification support vector machine based text recognition error correction grams algorithm health care professionals misidentified tablets adverse drug reaction ill health unknown tablets tablet images unknown images multiple feature sets text region support vector machine classifier post processing algorithm deletion errors corrected text template tablet database similar tablets common public

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Time-frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第2期12卷 169-175页

作者： Li, Xi Wang, Xiaohua Xie, Dingli Wang, Xinqiao Yang, Aijun Rong, Mingzhe Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

The ultra-high-frequency (UHF) method is efficient in partial discharges (PDs) detection in gas-insulated switchgear (GIS). The features extraction of UHF signals is significant for propagation characteristics analysis and PD pattern classification. The PD-induced UHF signals are acquired by the internal UHF sensors in an actual 252kV L-shaped GIS. The short-time Fourier transform method is applied to process UHF signals and describe the propagation characteristics in L-shaped GIS. Hu's invariant moments of energy density distribution are extracted as features in time-frequency plane. The features are utilised to discriminate different PD defect patterns in actual GIS model by the support vector machine classifier and achieve good results. Finally, a novel system of features extraction and classification of UHF signals is summarised.

关键词： time-frequency analysis gas insulated switchgear partial discharge measurement feature extraction signal detection signal classification UHF detectors UHF measurement Fourier transforms support vector machines Time-frequency analysis PD-induced UHF signal detection feature extraction ultrahigh-frequency method partial discharge detection gas-insulated switchgear propagation characteristics analysis PD pattern classification PD-induced UHF signal acquisition internal UHF sensor L-shaped GIS short-time Fourier transform method UHF signal processing Hu invariant moment energy density distribution extraction support vector machine classifier UHF signal classification voltage 252 kV

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A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

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BMC BIOINFORMATICS 2008年第1期9卷 1-15页

作者： Wei, Ning Flaschel, Erwin Friehs, Karl Nattkemper, Tim Wilhelm Univ Bielefeld Fac Technol Appl Informat Grp D-33501 Bielefeld Germany

Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results: This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion: The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature selection plays a role of exclu

关键词： Feature Selection Dead Cell Discrete Wavelet Transform support vector machine classifier Feature Subset

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Feature parameters extraction of power transformer PD signal based on texture features in TF representation

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第4期11卷 445-452页

作者： Zhou, Wenjun Liu, Yushun Li, Pengfei Wang, Yong Tian, Yan Wuhan Univ Sch Elect Engn Wuhan Peoples R China Guangzhou Power Supply Bur Co Ltd Guangzhou Guangdong Peoples R China

Ultra-high-frequency (UHF) method is an effective approach to power transformer partial discharge (PD) detection. The feature parameters extracted from UHF PD signal can be applied to insulation defect type recognition. In this study, a novel feature parameters extraction method based on texture features in time-frequency (TF) representation is proposed. PD detections of four typical insulation defects were performed on a 110kV oil-immersed power transformer. Time-domain waveform and corresponding TF representations or images of UHF PD signals were obtained. About 36 texture features were extracted from the grey-level co-occurrence matrix of TF images. The texture features were reduced into six new feature parameters by principal component analysis. These feature parameters were used as input of the support vector machine classifier for defect type recognition. The recognition accuracies of four kinds of typical defects reached 97.67, 97.00 97.67 and 98.33% proving that the proposed extracted feature parameters are suitable for insulation defect type recognition in power transformer.

关键词： feature extraction time-frequency analysis partial discharge measurement principal component analysis support vector machines power transformers feature parameter extraction texture features time-frequency representation ultrahigh-frequency method power transformer partial discharge detection time-domain waveform grey-level cooccurrence matrix principal component analysis support vector machine classifier defect type recognition

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