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A new pairwise kernel for biological network inference with support vector machines

BMC BIOINFORMATICS 2007年第10期8卷 1-10页

作者： Vert, Jean-Philippe Qiu, Jian Noble, William S. Ecole Mines Paris Ctr Computat Biol F-77300 Fontainebleau France Univ Washington Dept Genome Sci Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

Background: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). Results: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. Conclusion: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.

关键词： support vector machine Biological Network Diffusion Kernel Phylogenetic Profile support vector machine algorithm

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Characterization of digital medical images utilizing support vector machines

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BMC Medical Informatics and Decision Making 2004年第1期4卷 1-9页

作者： Maglogiannis, Ilias G. Zafiropoulos, Elias P. University of the Aegean Dept. of Info./Commun. Syst. Eng. Karlovasi Greece Sch. of Elec. and Comp. Engineering Natl. Technical University of Athens Athens Greece

Background: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using support vector machines and present the results of a preliminary study. Methods: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. Results: The SVM (support vector machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. Conclusion: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.

关键词： support vector machine Discriminant Analysis Kernel Function Thinness Ratio support vector machine algorithm

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Epithelial-mesenchymal transition status of circulating tumor cells in breast cancer and its clinical relevance

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Cancer Biology & Medicine 2020年第1期17卷 169-180页

作者： Jiaojiao Zhou Xuan Zhu Shijie Wu Jingxin Guo Kun Zhang Chunjing Xu Huihui Chen Yuxi Jin Yuting Sun Shu Zheng Yiding Chen Department of Surgical Oncology the Second Affiliated HospitalZhejiang University School of MedicineHangzhou 310000China The Key Laboratory of Cancer Prevention and Intervention China National Ministry of EducationZhejiang University School of MedicineHangzhou 310000China Life Science Institute Zhejiang UniversityHangzhou 310000China

Objective:Circulating tumor cells(CTCs)play a critical role in cancer metastasis,but their prevalence and significance remain *** study attempted to track the epithelial-mesenchymal transition(EMT)status of CTCs in breast cancer patients and investigate their clinical ***:In this study,the established negFACS-IF:E/M platform was applied to isolate rare CTCs and characterize their EMT status in breast cancer.A total of 89 breast cancer patients were recruited,including stage 0–III(n=60)and late stage(n=29)***:Using the negFACS-IF:E/M platform,it was found that in human epidermal growth factor receptor 2(HER2)+patients,mesenchymal CTCs usually exhibited a high percentage of HER2+*** IV breast cancer patients had considerably more CTCs than stage 0–III *** stage 0–III breast cancers,the HER2 subtype included a significantly higher percentage of mesenchymal and biphenotypic(epithelial and mesenchymal)CTCs than the luminal A or B *** stage IV patients,CTCs were predominantly epithelial in cases with local recurrence and were more mesenchymal in cases with distant *** applying a support vector machine(SVM)algorithm,the EMT status of CTCs could distinguish between breast cancer cases with metastasis/local recurrence and those without ***:The negFACS-IF:E/M platform provides a flexible and generally acceptable method for the highly sensitive and specific detection of CTCs and their EMT traits in breast *** study demonstrated that the EMT status of CTCs had high clinical relevance in breast cancer,especially in predicting the distant metastasis or local recurrence of breast cancer.

关键词： Circulating tumor cells breast cancer epithelial-to-mesenchymal transition estrogen receptor/human epidermal growth factor receptor 2 expression support vector machine algorithm

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machine learning approach in mortality rate prediction for hemodialysis patients

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第1期25卷 111-122页

作者： Radovic, Nevena Prelevic, Vladimir Erceg, Milena Antunovic, Tanja Univ Montenegro Elect Engn Dept Podgorica Montenegro Clin Ctr Montenegro Clin Nephrol Podgorica Montenegro Clin Ctr Montenegro Ctr Lab Diagnost Podgorica Montenegro

Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

关键词： Mortality rate prediction support vector machine algorithm K-means clustering algorithm chronic kidney disease

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Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

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BMC BIOINFORMATICS 2010年第1期11卷 1-11页

作者： Masso, Majid Vaisman, Iosif I. George Mason Univ Lab Struct Bioinformat Dept Bioinformat & Computat Biol Manassas VA 20110 USA

Background: HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An in silico mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions. Results: Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays. Conclusions: The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and

关键词： support vector machine Random Forest support vector machine algorithm Boost Decision Tree Delaunay Tessellation

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Assessment of PD severity in gas-insulated switchgear with an SSAE

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第4期11卷 423-430页

作者： Tang, Ju Jin, Miao Zeng, Fuping Zhang, Xiaoxing Huang, Rui Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Province Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China State Grid Shandong Elect Power Co Shandong Elect Power Res Inst Jinan 250002 Peoples R China

Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft-max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer-wise pre-training and supervised fine-tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.

关键词： partial discharge measurement gas insulated switchgear statistical analysis neural nets learning (artificial intelligence) encoding feature extraction computerised instrumentation PD severity assessment gas-insulated switchgear SSAE partial discharge severity assessment discharge time discharge amplitude deep-learning neural network model stacked sparse autoencoder feature extraction soft-max classifier unsupervised greedy layer-wise pre-training method supervised fine-tuning method support vector machine algorithm

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Modelling of supercapacitors based on SVM and PSO algorithms

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 502-507页

作者： Ding, Shichuan Hang, Jun Wei, Baolei Wang, Qunjing Anhui Univ Sch Elect Engn & Automat Hefei Anhui Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Tech Hefei Anhui Peoples R China

Supercapacitors present an attractive energy storage alternative for high-performance applications due to their compact size and high-power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

关键词： supercapacitors particle swarm optimisation support vector machines power engineering computing electric drives machine control supercapacitors modelling SVM algorithm PSO algorithm energy storage dynamic modelling method support vector machine algorithm particle swarm optimisation algorithm output voltage prediction temperature current initial voltage parameter optimization electric machine drive system frequent acceleration mode frequent mode frequent supercapacitor charging frequent supercapacitor discharging

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Return Strategy and machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 857595页

作者： Wang, Yuxuan Yang, Xiaoming Wang, Lili Hong, Zheng Zou, Wenjun Nanchang JiaoTong Inst Sports Inst Nanchang Peoples R China Univ Perpetual Help Syst Dalta Grad Sch Pinas Philippines Univ Putra Malaysia Fac Educ Studies Kuala Lumpur Malaysia East China Univ Technol Coll Phys Educ Nanchang Peoples R China Nanchang Univ Sch Software Nanchang Peoples R China

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimizati

关键词： OpenPose traversal dataset human motion characteristics machine learning support vector machine algorithm human motion recognition

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Detecting the penetration of malicious behavior in big data using hybrid algorithms

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 919-933页

作者： Wang, Yue Shi, Yan HeBei Chem & Pharmaceut Coll Shijiazhuang 050026 Hebei Peoples R China

Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusions into networks and information systems is one of the major issues in this subject. It is imperative and important to enhance intrusion detection skills in order to address malevolent behavior in large data. This paper presents a scalable approach to harmful data detection. Three variables have been considered in this strategy and model: scalability, user review, and temporal progress. High volumes of data can be processed using this technology. Time is split into time periods for data training in this system, and each time interval uses users' review information to train the data. Large volumes of data require sophisticated strategies to handle, and scalability in storage allows for faster processing and fewer computations. This approach is a kind of hardware-software hybrid solution for malware detection. A fresh approach to feature extraction has also been applied. In the suggested method, the bacteria algorithm in conjunction with the immune system algorithm has been utilized for the prediction operation, and the modified support vector machine algorithm and optical density have been utilized for classification. Based on the findings, the suggested combination algorithm outperforms other comparable techniques with a 21% detection rate, a 62% false alarm rate, a 15% accuracy rate, and a 73% training duration.

关键词： Intrusion detection Malicious behavior support vector machine algorithm Big data Bacterial system algorithm

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Investigating effects of force and pressure centre signals on stabilogram analysis

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第9期13卷 1305-1310页

作者： Cetin, Egehan Bilgin, Suleyman Akdeniz Univ Inst Nat Sci Dept Elect & Elect Engn Antalya Turkey Akdeniz Univ Fac Engn Dept Elect & Elect Engn Antalya Turkey

In this study, the centre of pressure (COPx) and force change (F-x) in the anterior-posterior (AP) plane, the centre of pressure (COPy) and force change (F-y) signals in medial-lateral (ML) plane were compared. In order to make comparisons in this context, the time-dependent norms of the COPx-COPy, and F-x-F-y signals (F-B, COPB) of 163 subjects were calculated separately. Subsequently, the mean standard deviation values (F-STD, COPSTD) of these values generated by time-based were obtained. The scatter plots in the AP plane $\lpar F_{x_{{\rm STD}}}\comma \;\, {\rm CO}{\rm P}_{x_{{\rm STD}}}\rpar $(FxSTD,COPxSTD) and the ML plane $\lpar F_{y_{{\rm STD}}}\comma \;\, {\rm CO}{\rm P}_{y_{{\rm STD}}}\rpar $(FySTD,COPySTD) are presented in different categories, which depend on the standard deviation values, young-elderly of the measurement group, the firm-soft of the ground and open-close of the eyes. Finally, the separation successes of $F_{x_{{\rm STD}}} - F_{y_{{\rm STD}}}$FxSTD-FySTD and ${\rm CO}{\rm P}_{x_{{\rm STD}}} - {\rm CO}{\rm P}_{y_{{\rm STD}}}$COPxSTD-COPySTD were calculated by means of basic classification algorithms. According to the results obtained, it is observed that the force signals generally provide more successful results than COP signals. In the study, the highest accuracy was performed with force signals and the support vector machine algorithm separated the young-aged group with an accuracy of 81.67%.

关键词： support vector machines biomechanics signal classification medical signal processing pressure centre signals anterior-posterior plane medial-lateral plane mean standard deviation values force signals COP signals force effects stabilogram analysis centre of pressure force change signals time-dependent norms scatter plots standard deviation values c classification algorithms support vector machine algorithm

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