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KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep auto-encoder

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第5期28卷 3178-3185页

作者： Kang, Wen-Yue Gao, Ying-Lian Wang, Ying Li, Feng Liu, Jin-Xing Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao 276826 Peoples R China Univ Hlth & Rehabil Sci Sch Hlth & Life Sci Qingdao 266113 Peoples R China

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.

关键词： CircRNA-disease association similarity network fusion auto-encoder multi-layer perceptron

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Self-Attention Enhanced auto-encoder for Link Weight Prediction With Graph Compression

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 89-99页

作者： Liu, Zhen Zuo, Wenbo Zhang, Dongning Zhou, Chuan Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China 54th Res Inst CETC Shijiazhuang 050081 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100045 Peoples R China

Predicting unobservable or missing weights over links on various real-world networks is of fundamental scientific significance in disparate disciplines like sociology, biology, and physics, as it has been revealed by scientists that link weights, representing strong or weak ties, are able to help us understand the mechanism of link formation, community growth, and network evolution. Nevertheless, previous studies mainly relied on shallow graph features to fulfill the task of link weight prediction and thus the proposed models have relatively poor predictive performance. By learning deep graph features to improve the predictive capability, a Self-attention Enhanced graph auto-encoder called SEA is proposed. To resolve the challenge of the model's scalability for large graph applications, a two-phased link weight prediction framework is further proposed, which comprises the modules of graph compression and SEA. Experiments on seven real-world networks (four uncompressed networks and three compressed networks) demonstrated that SEA can achieve an average 6% improvement of accuracy in missing link weight prediction against state-of-the-art methods. Some extended experiments and analyses verified that the proposed framework can significantly reduce the network's size by 60%-80% while maintaining the predictive power.

关键词： Predictive models Proteins Task analysis Evolution (biology) Computational modeling Visualization Time complexity auto-encoder graph compression link weight prediction self-attention mechanism

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An Efficient Encryption and Compression of Sensed IoT Medical Images Using auto-encoder

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 909-926页

作者： Passent El-kafrawy Maie Aboghazalah Abdelmoty M.Ahmed Hanaa Torkey Ayman El-Sayed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Math and Computer Science Department Faculty of ScienceMenoufia UniversityMenoufia32511Egypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

关键词： auto-encoder cloud image encryption IoT healthcare

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Anomaly Detection for Medical Images Using Heterogeneous auto-encoder

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 2770-2782页

作者： Lu, Shuai Zhang, Weihang Zhao, He Liu, Hanruo Wang, Ningli Li, Huiqi Beijing Inst Technol Beijing 100081 Peoples R China Univ Liverpool Dept Eye & Vis Sci Liverpool L7 8TX England Capital Med Univ Beijing Tongren Hosp Beijing Inst Ophthalmol Beijing 100054 Peoples R China Henan Prov Acad Med Sci Zhengzhou 450046 Peoples R China

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples;2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous auto-encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

关键词： Anomaly detection Magnetic resonance imaging auto-encoder heterogeneous network

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auto-encoder Based Co-training Multi-view Representation Learning 23rd

Auto-encoder Based Co-training Multi-view Representation Lea...

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23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Lu, Run-kun Liu, Jian-wei Wang, Yuan-fang Xie, Hao-jie Zuo, Xin China Univ Petr Dept Automat Beijing 102249 Peoples R China

ISBN: (纸本)9783030161422;9783030161415

Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views. The algorithm has two stages, the first is to train auto-encoder of each view, and the second stage is to train a supervised network. Interestingly, the two stages share the weights partly and assist each other by co-training process. According to the experimental result, we can learn a well performed latent feature representation, and auto-encoder of each view has more powerful reconstruction ability than traditional auto-encoder.

关键词： Multi-view auto-encoder Co-training

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auto-encoder Based Model for High-Dimensional Imbalanced Industrial Data 28th

Auto-Encoder Based Model for High-Dimensional Imbalanced Ind...

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28th International Conference on Neural Information Processing

作者： Zhang, Chao Bom, Sthitie Seagate Technol Bloomington MN 55435 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9783030923068;9783030923075

With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effectively consumed without the help of data-driven techniques. Deep learning is emerging as a promising technique to analyze these data, particularly in soft sensor modeling. The strong representational capabilities on complex data and the flexibility it offers from an architectural perspective make it an active topic of applied research in industrial settings. However, the successful applications of deep learning in soft sensing are still not widely integrated in factory control systems, because most of the research on soft sensing do not have access to large scale industrial data which are varied, noisy and incomplete. The results published in most research papers are therefore not easily reproduced when applied to the variety of data in industrial settings. Here we provide manufacturing data sets that are much larger and more complex than public open soft sensor data. Moreover, the data sets are from Seagate factories on active service with only necessary anonymization, so that they reflect the complex and noisy nature of real-world data. We introduce a variance weighted multi-headed quality-driven auto-encoder classification model that fits well into the high-dimensional and highly imbalanced data. Besides the use of weighting or sampling methods to handle the highly imbalanced data, the model also simultaneously predicts multiple outputs by exploiting output-supervised representation learning and multi-task weighting.

关键词： auto-encoder Big data Deep learning Process control Multi-task learning Soft sensing Wafer production

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Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 79卷 205-214页

作者： Chai, Xin Wang, Qisong Zhao, Yongping Liu, Xin Bai, Ou Li, Yongqiang Harbin Inst Technol Sch Elect Engn & Automat Harbin Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Dept Traff Informat & Control Engn Harbin Peoples R China Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA

In electroencephalography (EEG)-based emotion recognition systems, the distribution between the training samples and the testing samples may be mismatched if they are sampled from different experimental sessions or subjects because of user fatigue, different electrode placements, varying impedances, etc. Therefore, it is difficult to directly classify the EEG patterns with a conventional classifier. The domain adaptation method, which is aimed at obtaining a common representation across training and test domains, is an effective method for reducing the distribution discrepancy. However, the existing domain adaptation strategies either employ a linear transformation or learn the nonlinearity mapping without a consistency constraint;they are not sufficiently powerful to obtain a similar distribution from highly non-stationary EEG signals. To address this problem, in this paper, a novel component, called the subspace alignment auto-encoder (SAAE), is proposed. Taking advantage of both nonlinear transformation and a consistency constraint, we combine an auto-encoder network and a subspace alignment solution in a unified framework. As a result, the source domain can be aligned with the target domain together with its class label, and any supervised method can be applied to the new source domain to train a classifier for classification in the target domain, as the aligned source domain follows a distribution similar to that of the target domain. We compared our SAAE method with six typical approaches using a public EEG dataset containing three affective states: positive, neutral, and negative. Subject to-subject and session-to-session evaluations were performed. The subject-to-subject experimental results demonstrate that our component achieves a mean accuracy of 77.88% in comparison with a state-of-the-art method, TCA, which achieves 73.82% on average. In addition, the average classification accuracy of SAAE in the session-to-session evaluation for all the 15 subjects in

关键词： Emotion recognition EEG Domain adaptation auto-encoder MMD

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A semi-supervised auto-encoder using label and sparse regularizations for classification

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APPLIED SOFT COMPUTING 2019年 77卷 205-217页

作者： Chai, Zhilei Song, Wei Wang, Huiling Liu, Fei Jiangnan Univ Sch IoT Engn Wuxi Peoples R China Minist Educ Engn Res Ctr Internet Things Appl Technol Beijing Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi Peoples R China Wuxi Taihu Univ Sch IoT Engn Wuxi Peoples R China

The semi-supervised auto-encoder (SSAE) is a promising deep-learning method that integrates the advantages of unsupervised and supervised learning processes. The former learning process is designed to extract the underlying concepts of data as intrinsic information and enhance its generalization ability to express data. Furthermore, the supervised process tends to describe the rules of categorization with labels that further improve categorization accuracy. In this paper, we propose a novel semi-supervised learning method, namely, label and sparse regularization AE (LSRAE), by integrating label and sparse constraints to update the structure of the AE. The sparse regularization activates a minority of important neurons, while most of the other neurons are inhibited. Such a method ensures that LSRAE can yield a more local and informative structure of the data. Moreover, by implementing the label constraint, the supervised learning process can extract the features regulated by category rules and enhance the performance of the classifier in depth. To extensively test the performances of LSRAE, we perform our experiments on the benchmark datasets USPS, ISOLET and MNIST. The experimental results demonstrate the superiority of LSRAE in comparison with state-of-the-art feature extraction methods including AE, LSAE, SAE, ELM, DBN, and adaptive DBN. (C) 2019 Elsevier B.V. All rights reserved.

关键词： auto-encoder Semi-supervised learning Classification ELM DBN

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Estimation of gait normality index based on point clouds through deep auto-encoder

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2019年第1期2019卷 1页

作者： Trong-Nguyen Nguyen Meunier, Jean Univ Montreal DIRO Pavillon Andre Aisenstadt2920 Chemin Tour Montreal PQ H3T 1J4 Canada

This paper proposes a method estimating an index that indicates human gait normality based on a sequence of 3D point clouds representing the walking motion of a subject. A cylinder-based histogram is extracted from each cloud to reduce the number of data dimensions as well as highlight gait-related characteristics. A model of deep neural network is finally formed from such histograms of normal gait patterns to provide gait normality indices supporting gait assessment tasks. The ability of our approach is demonstrated using a dataset of 9 different gait types performed by 9 subjects and two other datasets converted from mocap data. The experimental results are also compared with other related methods that process different input data types including silhouette, depth map, and skeleton as well as state-of-the-art deep learning approaches working on point cloud.

关键词： Gait Normality index auto-encoder Deep network

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An auto-encoder-based summarization algorithm for unstructured videos

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第23期76卷 25039-25056页

作者： Han, Meng-Xiong Hu, Hai-Miao Liu, Yang Zhang, Chi Tian, Rong-Peng Zheng, Jin Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100083 Peoples R China Beijing Inst Graph Beijing 100029 Peoples R China Beihang Univ Digital Media Lab Beijing Peoples R China

Video summarization is an effective way to quick view videos and relieve the pressure of videos storage. However the traditional algorithms are hardly adapted to unstructured videos, due to the unobvious for scenes changing and ignoring the structure of the videos. Therefore, an auto-encoder-based summarization algorithm is proposed in this paper for unstructured videos. Each video structure is detected by an auto-encoder and both of the interestingness and representativeness of each video segment are predicted by the reconstruction errors of the segment. Meanwhile, most interesting and representative summarization is generated with the limited summary length. The experimental results show that the proposed algorithm obtained a better performance by comparing with the state-of-the-art.

关键词： Video summarization auto-encoder Video analysis

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