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A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第5期18卷 1801-1810页

作者： Li, Menglu Wang, Yanan Li, Fuyi Zhao, Yun Liu, Mengya Zhang, Sijia Bin, Yannan Smith, A. Ian Webb, Geoffrey I. Li, Jian Song, Jiangning Xia, Junfeng Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Inst Phys Sci & Informat Technol Hefei 230601 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Monash Univ Biomed Discovery Inst Melbourne Vic 3800 Australia Monash Univ Dept Biochem & Mol Biol Melbourne Vic 3800 Australia Monash Univ Fac Informat Technol Monash Ctr Data Sci Melbourne Vic 3800 Australia Monash Univ Ctr Impact AMR Dept Microbiol Melbourne Vic 3800 Australia Monash Univ Dept Biochem & Mol Biol Monash Ctr Data Sci Fac Informat Technol Melbourne Vic 3800 Australia Monash Univ Ctr Impact AMR Melbourne Vic 3800 Australia

Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.

关键词： Stakeholders Job shop scheduling Routing Schedules Medical services Delays Phage-host interaction bioinformatics sequence analysis deep learning pattern recognition multi-drug resistance

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One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment 28

One Comment from One Perspective: An Effective Strategy for ...

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28th international conference on Computational Linguistics, COLING 2020

作者： Huo, Tengfei Liu, Zhiqiang Zhang, Jinchao Zhou, Jie CAS Key Lab of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9781952148279

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin. © 2020 COLING 2020 - 28th international conference on Computational Linguistics, Proceedings of the conference. All rights reserved.

关键词： Music

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RDF Multi-query Optimization Algorithm Based on Triple pattern Reordering 15th

RDF Multi-query Optimization Algorithm Based on Triple Patte...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Wang, Manzi Xu, Fangfang Fu, Haidong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan430065 China

ISBN: (纸本)9783030325909

Under the premise of accelerating statistics by RDF storage index and narrowing the scope of semantic pruning, the distance-based triple pattern reordering algorithm is used to obtain the optimal connection order of a single query. After converting each query to a left deep tree, the left deep tree identification algorithm is used to find the common subtree and evaluate its cost. Establish materialized view and corresponding update replacement mechanism for valuable common subtrees. While optimizing a single query to a certain extent, it improves the possibility of the existence of common result sets among multiple queries. By making full use of query sharing, the overall execution cost of query set can be reduced. The experimental results show that the algorithm of this paper has better query performance than the existing query schemes, whether it is on single query or multiple query. Especially in the case that the RDF data set is large in scale, the number of queries in the query set is large, and the query statement is more complicated, the multi-query optimization method of this paper is better. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Clustering on the Basis of a Divisive Approach by the Method of Alternative Adaptation 4th

Clustering on the Basis of a Divisive Approach by the Method...

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4th international Scientific conference on Intelligent Information Technologies for Industry (IITI)

作者： Veselov, Gennady E. Lebedev, Boris K. Lebedev, Oleg B. Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783030500979

The paper presents a combined approach to clustering based on the integration of the divisive method with the methods of collective alternative adaptation. This group of methods is characterized by the sequential separation of the original cluster consisting of all objects, and the corresponding increase in the number of clusters. The objective is to enhance the convergence of the algorithm and the ability to exit from local optima, which allows you to work with large-scale problems and get high-quality results in a reasonable time. In this paper, the process of finding a solution is represented as an adaptive system. Under the influence of a series of adaptive actions, all objects (collective) are successively redistributed between the clusters. The goal of a specific object m(i) is to reach a state in which the total vector of forces acting on it from all objects placed in the same cluster with mi had the maximum value. The goal of the collective of objects is to achieve such a separation of objects into clusters, at which the minimum distance between a pair of objects belonging to different clusters has a maximum value. To implement the adaptation mechanism, each object mi is assigned an adaptation machine AAi. Studies have shown that the time complexity of the algorithm at one iteration has an estimate of O (n(2)), where n is the number of objects.

关键词： pattern recognition Clustering Collective alternative adaptation Automatic adaptation Hybrid algorithm

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Computer Vision for Astronomical Image Analysis

Computer Vision for Astronomical Image Analysis

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Sotiria Karypidou Ilias Georgousis George A. Papakostas MLV Research Group International Hellenic University Kavala Greece

ISBN: (纸本)9781665426565

Computer Vision (CV) is undoubtedly one of the most popular forms of Artificial Intelligence (AI) and its implementation has gained considerable ground in all aspects of our lives, from security and automotive, to the night sky observation and astronomy. In general, CV uses pattern recognition techniques for identifying objects in visual media (both static and moving images). The current archetype in CV is largely based on supervised AI, which uses large data sets of human-labelled images for training. Machine Learning (ML) and Deep Learning (DL) models in computer vision have undergone a period of extremely rapid development in recent past years; in particular for object recognition and localisation tasks. An area of study with great interest in practical applications that concerns this essay, is astronomical images analysis. However, one of the main challenges facing researchers these days is the existence of large quantities of annotated data sets, in the appropriate resolution and scale. This challenge consequently asks for huge amounts of storage and high computational power. In this paper, we systematically review and analyze different challenges faced by astronomers and continue with state-of-the-art methodologies that were conducted over the last decade.

关键词： Deep learning Training Computer vision Analytical models Visualization Image analysis Image resolution

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The Study of PSO-SVM and PSO-GRNN Algorithm Used in the Fault pattern Classification of Transformer 2020

The Study of PSO-SVM and PSO-GRNN Algorithm Used in the Faul...

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12th international conference on Machine Learning and computing, ICMLC 2020

作者： Shiling, Zhang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing Yubei China

ISBN: (纸本)9781450376426

Support Vector Machine and GR neural network are widely used in the fault pattern classification. For large data samples and fault types, these methods have high classification accuracy. In order to further improve the practical range and operational efficiency of numerical classifier, the Particle Swarm Optimization Algorithm is used to the optimize penalty and kernel function parameters of SVM, weight and threshold parameter of GRNN. By numerical verification, it is found that the classifier is applied to multi-peak complex curved surface function, has nice classification ability. The classifier is used in classification of DGA data of actual oil immersed, and it is found: running time of PSO-SVM classifier increases with the increase of the fault mode, and the recognition accuracy shows the trend of the overall decline. The classification accuracy of the PSO-GRNN classifier is relatively stable, but the fault classification is more time-consuming. Through the research and analysis of this paper, validity of optimization classification algorithm is verified by the simulation, compared performance of the PSO-GRNN and PSO-SVM classification algorithms, which provides ideas and methods for the fault classification method of oil immersed transformer. © 2020 ACM.

关键词： Support vector machines

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Partial discharge pattern recognition in gis based on s transform denoising

Partial discharge pattern recognition in gis based on s tran...

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22nd international Symposium on High Voltage Engineering (ISH 2021)

作者： Z. Wang Y. Zhao J. Guo Y. Su G. Liu L. Ding School of Electrical Engineering and Automation Hefei University of Technology Hefei People's Republic of China

With the development of urban power grid construction, the number of gas insulated switchgear (GIS) substations is increasing continuously, the internal space of GIS is very limited, the working field strength is very high, and the insulation margin is relatively small. Once the insulation defect of GIS appears, it is easy to cause equipment failure and lead to huge loss. Therefore, it is essential to detect the partial discharge signals of GIS in operation. In order to improve the recognition accuracy of PD patterns in GIS, a PD pattern recognition method based on S transform denoising was proposed in this paper. Firstly, partial discharge signals of simulated GIS models were collected by ultrasonic method, and then we used the S transform method to filter noise in partial discharge signals, then we extracted the signal statistical characteristics of partial discharge, using kernel principal component analysis (KPCA) to reduce the dimensionality of the extracted statistical features. Support vector machine (SVM) was applied to classify the partial discharge signals in this paper. The experimental results show that the S transform method is more effective than the traditional wavelet method in signal denosing of Bubble discharge, Floating discharge, Tip discharge and Creeping discharge in GIS. Based on the S transform denoising method, the recognition accuracy was higher than 90%.

关键词： feature extraction filtering theory gas insulated substations gas insulated switchgear partial discharges pattern recognition power engineering computing power grids principal component analysis signal denoising support vector machines wavelet transforms

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Smart Cyber-Physical System for pattern recognition of Illegal 3D Designs in 3D Printing 3rd

Smart Cyber-Physical System for Pattern Recognition of Illeg...

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3rd international conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems

作者： Vedeshin, Anton Dogru, John Mehmet Ulgar Liiv, Innar Ben Yahia, Sadok Draheim, Dirk 3D Control Syst Inc 3DPrinterOS San Francisco CA 94103 USA Tallinn Univ Technol Dept Software Sci Tallinn Estonia Tallinn Univ Technol Informat Syst Grp Tallinn Estonia

ISBN: (纸本)9783030451820;9783030451837

The method to protect intellectual property (IP) in automated manufacturing (AM) and 3D printing industry particularly, presented in this paper, is based on a smart cyber-physical system and the radical improvement of preventive and detective controls to find potential cases of automated manufacturing copyrights infringement. The focus of this paper is not the ecosystem of managing a large network of physical 3D printers, but a smart application and data analysis of data flow within the ecosystem to solve a problem of IP protection and illegal physical objects manufacturing. In this paper, we focus on the first step in this direction - pattern recognition of illegal physical designs in 3D printing, and detection of firearms parts particularly. The proposed method relies on several important steps: normalization of 3D designs, metadata calculation, defining typical illegal designs, pattern matrix creation, new 3D designs challenging, and pattern matrix update. We classify 3D designs into loose groups without strict differentiation, forming a pattern matrix. We use conformity and seriation to calculate the pattern matrix. Then, we perform the analysis of the matrix to find illegal 3D designs. Our method ensures simultaneous pattern discovery at several information levels - from local patterns to global. We performed experiments with 5831 3D designs, extracting 3728 features. It took 12 min to perform pattern matrix calculation based on the test data. Each new 3D design file pattern recognition took 0.32 s on four core, 8GB ram, 32GB SSD Azure VM instance.

关键词： pattern recognition Intelligent manufacturing systems Technology social factors Distributed computing

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2nd international conference on Computational Intelligence in pattern recognition, CIPR 2020

2nd International Conference on Computational Intelligence i...

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2nd international conference on Computational Intelligence in pattern recognition, CIPR 2020

ISBN: (纸本)9789811524486

The proceedings contain 50 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Deep Learning-Based Automated Feature Engineering for Rice Leaf Disease Prediction;literature Survey on Log-Based Anomaly Detection Framework in Cloud;feature Extraction and Disease Prediction from Paddy Crops Using Data Mining Techniques;estimating software Effort Using Neural Network: An Experimental Investigation;statistical Analysis on Efficacy of Yagya Therapy for Type-2 Diabetic Mellitus Patients Through Various Parameters;a Proposed Gene Selection Approach for Disease Detection;Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear Data;elephant Herding Optimization for Multiprocessor Task Scheduling in Heterogeneous Environment;analysis of Parallel M5P and Random Forest Regression for Visualization of Traffic Behavior;nearest Neighbor-Based Differential Evolution for Reconstructing Gene Regulatory Network;software Effort Estimation Using Particle Swarm Optimization: Advances and Challenges;modern Trends in Monitoring and Analysis of Chemical Pesticides by Using Artificial Neural Networks;semi-supervised Change Detection Technique on Remote Sensing Images Using Posterior Probabilities;Feature Engineering for Cardio-Thoracic Disease Detection from NIH Chest Radiographs;wireless Body Area Network: An Application of IoT and Its Issuses—A Survey;enhancing Security for Communication in Wireless Sensor Network;dimension Reduction of Microarray Data Using Gene Ontology and Correlation Filter;modified Contrast Enhancement Algorithm Using Numerical Integration Simpson’s Method;WHMS: An Efficient Wireless NoC Design for Better Communication Efficiency;data Hiding Based on Decagon-Shaped Shell;inference-Based Statistical Analysis for Suspicious Activity Detection Using Facial Analysis;lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status.

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Machine Learning Methods for Predictive Analytics in Health Care

Machine Learning Methods for Predictive Analytics in Health ...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Appoorva Bansal Anand Kr. Shukla Shreya Bansal Assistant Professor Chandigarh University Punjab Professor Chandigarh University Punjab

ISBN: (纸本)9781665439718

When we are dealing with the huge amount of data we have to look forward to the techniques like machine learning, predictive analysis, pattern recognition etc. Specially in the health sector machine learning is growing too fast and also give productive challenges. For predicting future, machine learning algorithm plays an important role, through which system can learn and become productive by the passing time. Method and Techniques of Machine Learning are used in various fields. Among those Health care is of the field which takes lots of help from the techniques of predictions. Through the techniques of predictive analysis in health care, effective treatment and risk factor can be managed effectively among patients and improve the quality of health care. As per the modern scenario, there is a need of huge improvement in the healthcare in term of cost and other factors. Today healthcare sector faces problem in the electronic data management, disease prediction as per the symptoms, patient classification, computer based diagnosis, risk factor etc. These challenges can be solved with the help of the machine learning tools and techniques. In this paper the focus to study the various machine learning method for the predictive analysis. Its includes various application area of machine learning, but mainly highlighting the role of machine learning in health care sectors.

关键词： Machine learning algorithms Volume measurement soft sensors Machine learning Medical services pattern recognition Security

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