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A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction

CURRENT BIOINFORMATICS 2024年第4期19卷 341-351页

作者： Wei, Meng-Meng Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Lei-Wang, Lei- Xijing Univ Sch Informat Engn Xian Peoples R China Longdong Univ Coll Agr & Forestry Qingyang Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Guangxi Acad Sci Guangxi Key Lab Human Machine Interact & Intellige Nanning Peoples R China

Background LncRNA is not only involved in the regulation of the biological functions of protein-coding genes, but its dysfunction is also associated with the occurrence and progression of various diseases. Various studies have shown that an in-depth understanding of the mechanism of action of lncRNA is of great significance for disease treatment. However, traditional wet testing is time-consuming, laborious, expensive, and has many subjective factors which may affect the accuracy of the *** Most of the methods for predicting lncRNA-protein interaction (LPI) rely on a single feature, or there is noise in the feature. To solve this problem, we proposed a computational model, CSALPI based on a deep neural *** Firstly, this model utilizes cosine similarity to extract similarity features for lncRNA-lncRNA and protein-protein, denoising similar features using the sparse autoencoder. Second, a neighbor enhancement autoencoder is employed to enforce neighboring nodes to be represented similarly by reconstructing the denoised features. Finally, a Light Gradient Boosting Machine classifier is used to predict potential *** To demonstrate the reliability of CSALPI, multiple evaluation metrics were used under a 5-fold cross-validation experiment, and excellent results were achieved. In the case study, the model successfully predicted 7 out of 10 disease-associated lncRNA and protein *** The CSALPI can be an effective complementary method for predicting potential LPIs from biological experiments.

关键词： lncRNA-protein interactions lncRNA protein cosine similarity sparse autoencoder neighbor enhancement autoencoder

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Multi-lead model-based ECG signal denoising by guided filter

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 79卷 34-44页

作者： Hao, Huaqing Liu, Ming Xiong, Peng Du, Haiman Zhang, Hong Lin, Feng Hou, Zengguang Liu, Xiuling Hebei Univ Key Lab Digital Med Engn Hebei Prov Coll Elect & Informat Engn Baoding Peoples R China Hebei Univ Affiliated Hosp Baoding Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Chinese Acad Sci Inst Automat Beijing Peoples R China

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.

关键词： Electrocardiograph (ECG) denoising Multi-lead model-based ECG signal Guided filter sparse autoencoder

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A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of US

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ENERGY 2021年 235卷 121216-121216页

作者： Qiao, Weibiao Liu, Wei Liu, Enbin Yan Shan Univ Sch Vehicle & Energy Qinhuangdao 066004 Hebei Peoples R China Chongqing Univ State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400044 Peoples R China Southwest Petr Univ Petr Engn Sch Chengdu 61000 Peoples R China

The prediction model's performance in view of the wavelet transform (WT) is affected because the wavelet basis function (WBF) and its orders and layers are determined randomly. To solve this problem, this research devises a crossover experiment with 160 components of each WBF (Coiflets and Symlets) and forecasts 320 schemes with sparse autoencoder (SAE) and long short-term memory (LSTM), developing a combination model with WT, SAE, and LSTM. Furthermore, to verify the performance of the combination prediction model, the difference between the natural gas production and consumption in the U.S. is determined which is taken as an example. The results indicate that the SAE-LSTM exceeds other AI models (e.g. ELM), and WT outperforms other preprocessing algorithms (e.g. EMD) based on forecasting accuracy. The best performance of the established model is obtained by using the two orders six layers of Coiflets, and six orders seven layers of Symflets for natural gas production and consumption. In addition, the average difference between consumption and production of natural gas is 10.6809 Bcfpd. To make up for such a gap, some import methods can be adopted. It can be concluded that this study can provide a reference for other time-series prediction and natural gas policymakers. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Natural gas production and consumption Long short-term memory Wavelet transform sparse autoencoder Difference prediction

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Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第8期31卷 1899-1916页

作者： Li, Jialin Li, Xueyi He, David Qu, Yongzhi Northeastern Univ Sch Mech Engn & Automat Shenyang 110000 Peoples R China Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Univ Minnesota Dept Mech & Ind Engn Duluth MN 55812 USA

In recent years, deep learning based diagnostic approaches have become more attractive. However, most of these methods are supervised diagnostic approaches. Developing a supervised diagnostic model requires a large number of labeled training data. And it is time consuming and labor intensive to obtain labeled data for a variety of systems and working conditions. Therefore, an unsupervised diagnostic model that does not require labeled training data is more desirable. This paper proposes an unsupervised diagnostic model by integrating a sparse autoencoder, a deep belief network, and a binary processor. In comparison with the existing unsupervised methods, the proposed method does not need to perform statistical features extraction, and directly uses the normalized frequency domain signals as the inputs. Moreover, in the proposed diagnostic model, the input data is passed through layer by layer without fine-tuning, which is completely unsupervised process. The proposed methods have been validated with bearing fault datasets and gear pitting fault datasets. The validation results show that the proposed method has a higher accuracy for both bearing and gear pitting fault diagnosis.

关键词： Fault diagnosis sparse autoencoder Deep belief network Bearing fault Gear pitting fault

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Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis

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ISA TRANSACTIONS 2019年 95卷 306-319页

作者： Yang, Jing Xie, Guo Yang, Yanxi Zhang, Youmin Liu, Wei Xian Univ Technol Sch Automat & Informat Engn Jinhua South Rd Xian Shaanxi Peoples R China Concordia Univ 1455 de Maisonneuve Blvd W Montreal PQ H3G 1M8 Canada Tianshui Normal Univ Sch Mechatron & Automot Engn Xihe South Rd Tianshui Peoples R China

Currently, single fault diagnosis has received mass concern, and the related research achievements are remarkable. However, because of the mutual interaction of subsystems and the coupling of faults characteristics, the diagnosis of multiple intermittent faults commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an improved Constrained sparse autoencoder integrated with Correlation Analysis (CA-CSAE) is proposed, further, a diagnosis scheme for multiple intermittent faults is formulated in this paper. The main strategies are as follows. (1) An adaptive loss function and a constraint for initial weight are designed to improve the diversity and accuracy of SAE feature learning. (2) A relational constraint term is constructed to mitigate the effect of data correlation. (3) The evaluation criterion of data correlation degree is put forward to quantify the scope of the method. (4) In order to improve the diagnostic efficiency, ReLU is introduced as the activation function of hidden layer, and L-BFGS algorithm is employed to obtain the optimal solution. (5) Softmax classifier is employed as the output layer to identify fault mode and ensure the reliability of diagnosis results. Finally, comparison experiments and results analysis are conducted to verify the effectiveness and practicability of the proposed method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： sparse autoencoder Correlation analysis Intermittent faults Fault diagnosis

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SD-MSAEs: Promoter recognition in human genome based on deep feature extraction

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年 61卷 55-62页

作者： Xu, Wenxuan Zhang, Li Lu, Yaping Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Joint Int Res Lab Machine Learning & Neuromorph Suzhou 215006 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210000 Jiangsu Peoples R China

The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAE5. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Context features Promoter recognition sparse autoencoder Statistical divergence Support vector machine

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Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

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IEEE ACCESS 2022年 10卷 120013-120022页

作者： Wang, Hongxin Xu, Degang Wen, Xin Song, Jinsheng Li, Linwen Cent South Univ Sch Automation Changsha Peoples R China Shanghai Jiao Tong Univ Sch Mech & Power Engn Shanghai Peoples R China Shanghai Aircraft Design & Res Inst Shanghai Peoples R China

Fault diagnosis and classification (FDC) is an important part of prognostics and health management for ensuring safety and performance in the flight. However, it is challenging to achieve accurate FDC only based on single senor readings. In this paper, a fused FDC model among multiple different sensors is stabled by a hybrid deep learning architecture combining a sparse autoencoder (SAE) and a convolutional neural network (CNN). The hybrid model uses the SAE to enhance the hidden fault signal features in the multiple sensor signals, and then classifies the obtained feature map using the CNN. This method, which combines the advantages of the SAE in feature extraction and of the CNN in local feature recognition, fully utilizes the spatiotemporal coupling characteristics of multi-sensor signals. The FDC accuracy obtained by the proposed method when applied to a flight test data set is 93.78%, compared with 66.67% obtained using the combined SAE and feedforward neural network method and 83.11% obtained using the CNN only.

关键词： Convolutional neural network fault classification flight test data fault diagnosis sparse autoencoder

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A mixed intelligent condition monitoring method for nuclear power plant

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ANNALS OF NUCLEAR ENERGY 2020年 140卷 107307-107307页

作者： Peng, Binsen Xia, Hong Ma, Xintong Zhu, Shaomin Wang, Zhichao Zhang, Jiyu Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China

Nuclear power plant is a highly safety required system which has multi- operating condition in different power mode, and it requires a more advanced technology to realize condition monitoring. To improve the condition monitoring techniques, a mixed condition monitoring method based on sparse autoencoder and isolation forest is proposed to realize the condition monitoring of nuclear power plant, where sparse autoencoder is responsible for data feature extraction and dimensionality reduction, and isolation forest is responsible for the anomaly monitoring of nuclear power plant. The proposed method can transform high-dimensional data into a low-dimensional space, remove the redundancy of the data, and then identify the state through a high-performance monitoring model, thereby improving monitoring efficiency and accuracy. In order to expound the performance of the condition monitoring model proposed in this paper, we select one operating condition and two operating conditions for testing. We also obtained the condition monitoring results of local outlier factor and one-class support vector machine to compare with our method. From the results, it can be known that sparse autoencoder can extract the nature of operating data, and monitoring accuracy of 100% and 98% can be achieved under one operating condition and two operating conditions by isolation forest method, respectively. Compared with other methods, the proposed method has obvious advantages. This research has important implications for the condition monitoring of nuclear power plant and the system with multi-operating conditions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nuclear power plant Intelligent Condition monitoring sparse autoencoder Isolation forest

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An improved deep network for intelligent diagnosis of machinery faults with similar features

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第12期14卷 1851-1864页

作者： Yang, Jing Xie, Guo Yang, Yanxi Li, Xin Mu, Lingxia Takahashi, Sei Mochizuki, Hiroshi Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Nihon Univ Coll Sci & Technol Dept Comp Engn Funabashi Chiba 2748501 Japan

Currently, fault diagnosis, especially single fault diagnosis, has yielded fruitful research results. However, for concurrent faults, which exist more widely in real industrial systems. Due to the challenges of complex model mechanism, single and coupled monitoring data, as well as difficulty distinguished similar features, causing the existing research findings on concurrent faults have not yet achieved satisfactory effects. Considering the problem and in order to extract features automatically while enhancing the diagnostic accuracy, an improved sparse autoencoder (SAE) network for intelligent diagnosis is proposed. Specifically, the main contents are as follows: firstly, the original time-domain signals are preprocessed to eliminate the noise disturbance. The processed signals are then adopted as the input of diagnostic network. Secondly, an initial weight constraint and an adaptive loss function are constructed to improve the diversity of SAE feature learning. Thirdly, limited-memory quasi-newton algorithm (L-BFGS) algorithm is used to solve the optimal solution and the machinery health condition identification is achieved by softmax classifier. Finally, the verification experiments based on the monitoring data obtained from different machinery are conducted. The results demonstrate the effectiveness and practicability of the proposed method. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： deep learning sparse autoencoder concurrent faults similar features fault diagnosis

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No-reference image quality assessment using statistical characterization in the shearlet domain

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2014年第7期29卷 748-759页

作者： Li, Yuming Po, Lai-Man Xu, Xuyuan Feng, Litong City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Image and video quality measurements are crucial for many applications, such as acquisition, compression, transmission, enhancement, and reproduction. Nowadays, no-reference (NR) image quality assessment (IQA) methods have drawn extensive attention because it does not rely on any information of original images. However, most of the conventional NR-IQA methods are designed only for one or a set of predefined specific image distortion types, which are unlikely to generalize for evaluating image/video distorted with other types of distortions. In order to estimate a wide range of image distortions, in this paper, we present an efficient general-purpose NR-IQA algorithm which is based on a new multiscale directional transform (shearlet transform) with a strong ability to localize distributed discontinuities. This is mainly based on distorted natural image that leads to significant variation in the spread discontinuities in all directions. Thus, the statistical property of the distorted image is significantly different from that of natural images in fine scale shearlet coefficients, which are referred to as 'distorted parts'. However, some 'natural parts' are reserved in coarse scale shearlet coefficients. The algorithm relies on utilizing the natural parts to predict the natural behavior of distorted parts. The predicted parts act as 'reference' and the difference between the reference and distorted parts is used as an indicator to predict the image quality. In order to achieve this goal, we modify the general sparse autoencoder to serve as a predictor to get the predicted parts from natural parts. By translating the NR-IQA problem into classification problem, the predicted parts and distorted parts are utilized to form features and the differences between them are identified by softmax classifier. The resulting algorithm, which we name SHeArlet based No-reference Image quality Assessment (SHANIA), is tested on several database (LIVE, Multiply Distorted LIVE and TID2008

关键词： No-reference image quality assessment Shearlet transform sparse autoencoder Softmax classification

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