Vehicle Re-identification Using Joint Pyramid Feature Representation Network

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

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An Integrated Prediction Method for Identifying Protein-Protein Interactions

Current Proteomics ◽

10.2174/1570164616666190306152318 ◽

2020 ◽

Vol 17 (4) ◽

pp. 271-286

Author(s):

Chang Xu ◽

Limin Jiang ◽

Zehua Zhang ◽

Xuyao Yu ◽

Renhai Chen ◽

...

Keyword(s):

Protein Interactions ◽

Feature Vector ◽

Prediction Method ◽

Feature Representation ◽

Protein Interaction Networks ◽

Learning Approach ◽

Biological Processes ◽

Integrated Learning ◽

Protein Protein Interactions ◽

Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

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A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering

Current Bioinformatics ◽

10.2174/1574893614666190126144139 ◽

2019 ◽

Vol 14 (6) ◽

pp. 480-490 ◽

Cited By ~ 1

Author(s):

Tuncay Bayrak ◽

Hasan Oğul

Keyword(s):

Gene Expression ◽

Regression Model ◽

Collaborative Filtering ◽

High Throughput Sequencing ◽

Mean Squared Error ◽

Experimental Studies ◽

Kernel Functions ◽

Feature Representation ◽

Sequencing Analysis ◽

Computational Systems Biology

Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis. Methods: Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples. Results: According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model. Conclusion: The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.

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Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Current Bioinformatics ◽

10.2174/1574893614666181217145156 ◽

2019 ◽

Vol 14 (5) ◽

pp. 406-421 ◽

Cited By ~ 3

Author(s):

Ting-He Zhang ◽

Shao-Wu Zhang

Keyword(s):

Machine Learning ◽

Feature Fusion ◽

Protein Sequences ◽

Subcellular Location ◽

Automated Analysis ◽

Cellular Level ◽

Machine Learning Algorithms ◽

Feature Representation ◽

Protein Subcellular Location ◽

Protein Subcellular Locations

Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

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Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2019.2938890 ◽

2020 ◽

Vol 16 (6) ◽

pp. 3721-3730 ◽

Cited By ~ 41

Author(s):

Xiaofeng Yuan ◽

Jiao Zhou ◽

Biao Huang ◽

Yalin Wang ◽

Chunhua Yang ◽

...

Keyword(s):

Deep Learning ◽

Learning Strategy ◽

Soft Sensor ◽

Feature Representation ◽

Relevant Feature ◽

Sensor Modeling

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Meta Learning for Few-Shot One-Class Classification

AI ◽

10.3390/ai2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 195-208

Author(s):

Gabriel Dahia ◽

Maurício Pamplona Segundo

Keyword(s):

Feature Representation ◽

Support Vector ◽

Support Vector Data Description ◽

Target Class ◽

Feature Representations ◽

Shot Classification ◽

Training Stage ◽

Comparable Performance ◽

Meta Learning ◽

One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

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Weighted statistical binary patterns for facial feature representation

Applied Intelligence ◽

10.1007/s10489-021-02477-1 ◽

2021 ◽

Author(s):

Hung Phuoc Truong ◽

Thanh Phuong Nguyen ◽

Yong-Guk Kim

Keyword(s):

Comprehensive Evaluation ◽

Facial Feature ◽

Input Image ◽

Feature Representation ◽

Illumination Variation ◽

Straight Line ◽

Mean And Variance ◽

The Mean ◽

Face Datasets ◽

Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

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Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽

10.3390/s21041054 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1054

Author(s):

Guo Bi ◽

Shan Liu ◽

Shibo Su ◽

Zhongxue Wang

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Condition Monitoring ◽

Time Domain ◽

Diamond Wheel ◽

Feature Representation ◽

Grinding Wheel ◽

Time Waveform ◽

Original Time ◽

Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

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An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

Complex & Intelligent Systems ◽

10.1007/s40747-021-00442-6 ◽

2021 ◽

Author(s):

Cong Gao ◽

Ping Yang ◽

Yanping Chen ◽

Zhongmin Wang ◽

Yue Wang

Keyword(s):

Time Series ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Estimation Method ◽

Feature Representation ◽

Sensor Data ◽

Wireless Sensor ◽

Data Sets ◽

Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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