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AFExNet: An adversarial autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2060-2070页

作者： Mondol, Raktim Kumar Nhan Duy Truong Reza, Mohammad Ippolito, Samuel Ebrahimie, Esmaeil Kavehei, Omid RMIT Univ Dept Elect & Biomed Engn Melbourne Vic 3000 Australia Univ Queensland Ctr Adv Imaging St Lucia Qld 4072 Australia La Trobe Univ Coll Sci Hlth & Engn Sch Life Sci Genom Res Platform Melbourne Vic 3086 Australia Univ Adelaide Sch Anim & Vet Sci Adelaide SA 5371 Australia Univ Melbourne Sch BioSci Parkville Vic 3010 Australia Univ Sydney Sch Biomed Engn Camperdown NSW 2006 Australia

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://***/NeuroSyd/breast-cancer-sub-types.

关键词： Feature extraction Breast cancer Gene expression Genomics Deep learning Biological information theory Bioinformatics Neural network deep learning genetic data feature extraction adversarial autoencoder

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druGAN: An Advanced Generative adversarial autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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MOLECULAR PHARMACEUTICS 2017年第9期14卷 3098-3104页

作者： Kadurin, Artur Nikolenko, Sergey Khrabrov, Kuzma Aliper, Alex Zhavoronkov, Alex Johns Hopkins Univ Eastern Emerging Technol Ctr Insilico Med Inc Pharmaceut Artificial Intelligence Dept Baltimore MD 21218 USA Natl Res Univ Higher Sch Econ St Petersburg 190008 Russia Steklov Math Inst St Petersburg St Petersburg 191023 Russia Mail Ru Grp Ltd Search Dept Moscow 125167 Russia Biogerontol Res Fdn Trevissome Pk Truro TR4 8UN England Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia Kazan Fed Univ Kazan 420008 Republic Of Tat Russia

Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints;(b) capacity of processing very large molecular data sets;and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.

关键词： adversarial autoencoder deep learning drug discovery variational autoencoder generative adversarial network

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Missing Value Imputation Model Based on adversarial autoencoder Using Spatiotemporal Feature Extraction

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Intelligent Automation & Soft Computing 2023年第8期37卷 1925-1940页

作者： Dong-Hoon Shin Seo-El Lee Byeong-Uk Jeon Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-siGyeonggi-do16227Korea

Recently,the importance of data analysis has increased significantly due to the rapid data *** particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing *** missing values in data lead to the decreased predictive performance of *** missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image ***,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication *** study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these *** proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial *** results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive *** this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

关键词： Missing value adversarial autoencoder spatiotemporal feature extraction

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Purifying adversarial Images Using adversarial autoencoder With Conditional Normalizing Flows

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2023年 4卷 267-274页

作者： Ji, Yi Le, Trung-Nghia Nguyen, Huy H. H. Echizen, Isao Univ Tokyo Tokyo 1138654 Japan Natl Inst Informat Tokyo 1018430 Japan

We present a target-agnostic adversarial autoencoder with conditional normalizing flows specifically designed to, given any unlabeled image dataset, purify adversarial samples into clean images, i.e., remove adversarial noise from the images while preserving their visual quality. In our model interpretation, samples are processed by manifold projection in which the encoder brings the sample back into a posterior data distribution in latent space so that the sample is less likely to be irregular to the learned representation of any target classifier. Normalizing flows conditioned on top of our hybrid network structure and walk-back training are used to deal with common drawbacks of generative model and autoencoder-based approaches: not only the trade-off between compression loss and over-fitting on training data but also the structural model dependency on dataset classes and labels. Experiments demonstrated that our proposed model is preferable to existing target-agnostic adversarial defense methods particularly for large and unlabeled image datasets.

关键词： adversarial autoencoder adversarial purification normalizing flow representation learning

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adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate

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JOURNAL OF BIOMECHANICS 2020年 103卷 109684-000页

作者： Hernandez, Vincent Kulic, Dana Venture, Gentiane Univ Waterloo Elect & Comp Engn Waterloo ON Canada Monash Univ 14 Alliance LaneClayton Campus Clayton Vic 3800 Australia Tokyo Univ Agr & Technol Dept Mech Syst Engn Tokyo Japan

The ability to visualize and interpret high dimensional time-series data will be critical as wearable and other sensors are adopted in rehabilitation protocols. This study proposes a latent space representation of high dimensional time-series data for data visualization. For that purpose, a deep learning model called adversarial autoencoder (AAE) is proposed to perform efficient data dimensionality reduction by considering unsupervised and semi-supervised adversarial training. Eighteen subjects were recruited for the experiment and performed two sets of exercises (upper and lower body) on the Wii Balance Board. Then, the accuracy of the latent space representation is evaluated on both sets of exercises separately. Data dimensionality reduction with conventional Machine Learning (ML) and supervised Deep Learning (DL) classification are also performed to compare the efficiency of AAE approaches. The results showed that AAE can outperform conventional ML approaches while providing close results to DL supervised classification. AAE approaches for data visualization are a promising approach to monitor the subject's movements and detect adverse events or similarity with previous data, providing an intuitive way to monitor the patient's progress and provide potential information for rehabilitation tracking. (C) 2020 Published by Elsevier Ltd.

关键词： Human activity recognition Ground reaction force adversarial autoencoder Deep learning Machine learning

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adversarial autoencoder for continuous sign language recognition

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第22期36卷

作者： Kamal, Suhail Muhammad Chen, Yidong Li, Shaozi Xiamen Univ Sch Informat Xiamen Fujian Peoples R China Xiamen Univ Key Lab Digital Protect & Intelligent Proc Intangi Minist Culture & Tourism Xiamen Fujian Peoples R China Bayero Univ Kano Fac Comp Dept Informat Technol Kano Nigeria

Sign language serves as a vital communication medium for the deaf community, encompassing a diverse array of signs conveyed through distinct hand shapes along with non-manual gestures like facial expressions and body movements. Accurate recognition of sign language is crucial for bridging the communication gap between deaf and hearing individuals, yet the scarcity of large-scale datasets poses a significant challenge in developing robust recognition technologies. Existing works address this challenge by employing various strategies, such as enhancing visual modules, incorporating pretrained visual models, and leveraging multiple modalities to improve performance and mitigate overfitting. However, the exploration of the contextual module, responsible for modeling long-term dependencies, remains limited. This work introduces an adversarial autoencoder for Continuous Sign Language Recognition, AA-CSLR, to address the constraints imposed by limited data availability, leveraging the capabilities of generative models. The integration of pretrained knowledge, coupled with cross-modal alignment, enhances the representation of sign language by effectively aligning visual and textual features. Through extensive experiments on publicly available datasets (PHOENIX-2014, PHOENIX-2014T, and CSL-Daily), we demonstrate the effectiveness of our proposed method in achieving competitive performance in continuous sign language recognition.

关键词： adversarial autoencoder continuous sign language recognition vision-language

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Automatic eNodeB state management in LTE networks using Semi-Supervised Learning with adversarial autoencoder 21

Automatic eNodeB state management in LTE networks using Semi...

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21st IEEE International Conference on High Performance Switching and Routing (IEEE HPSR)

作者： Hara, Kazuki Shiomoto, Kohei Eng, Chin Lam Backstad, Sebastian Univ Tsukuba Ibaraki Japan Tokyo City Univ Tokyo Japan Ericsson Japan Yokohama Kanagawa Japan

ISBN: (纸本)9781728148465

It is crucial to identify the cause immediately when a failure occurs at the base station called eNodeB in LTE networks. However, a huge amount of log data generated from the eNodeB prevents the human operator to quickly identify the cause of failure. In order to improve the network operation efficiency, machine learning technique is used to analyze Key Performance Indicator (KPI) data generated from eNodeB and classify the operational status of the eNodeB. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate raw performance metric data. To address this issue, we propose a method that employs adversarial autoencoder (AAE), which is a semi-supervised learning method. We evaluate the proposed method using eNodeB log data collected from a service provider LTE network. We confirm that our approach achieves on average 94% accuracy and yields high accuracy even for the class with a small amount of labeled data.

关键词： LTE network Machine learning Deep learning Semi-supervised learning adversarial autoencoder

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Image-based Process Monitoring via adversarial autoencoder with Applications to Rolling Defect Detection 15

Image-based Process Monitoring via Adversarial Autoencoder w...

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15th IEEE International Conference on Automation Science and Engineering (IEEE CASE)

作者： Yan, Hao Yeh, Huai-Ming Sergin, Nurettin Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

ISBN: (纸本)9781728103563

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high-dimensionality and complex spatial structures. Recent advancements in unsupervised deep models such as generative adversarial networks (GAN) and adversarial autoencoders (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique. Based on this, we propose a monitoring statistic efficiently capturing the change of the data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.

关键词： Statistical Process Control Profile Monitoring Deep Generative Models adversarial autoencoder

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Active Attack Detection Based on Interpretable Channel Fingerprint and adversarial autoencoder

Active Attack Detection Based on Interpretable Channel Finge...

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IEEE International Conference on Communications (ICC)

作者： Ji, Zijie Yang, Binbing Yeoh, Phee Lep Zhang, Yan He, Zunwen Li, Yonghui Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fullysupervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.

关键词： Active attack detection adversarial autoencoder deep learning physical layer security

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AA-LSTM: An adversarial autoencoder Joint Model for Prediction of Equipment Remaining Useful Life 1

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25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Zhu, Dong Wu, Chengkun Xu, Chuanfu Wang, Zhenghua Natl Univ Def Technol Coll Comp Sci & Technol Inst Quantum Informat Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol State Key Lab High Performance Comp Changsha Peoples R China

ISBN: (数字)9783030757625

ISBN: (纸本)9783030757625;9783030757618

Remaining Useful Life (RUL) prediction of equipment can estimate the time when equipment reaches the safe operating limit, which is essential for strategy formulation to reduce the possibility of loss due to unexpected shutdowns. This paper proposes a novel RUL prediction model named AA-LSTM. We use a Bi-LSTM-based autoencoder to extract degradation information contained in the time series data. Meanwhile, a generative adversarial network is used to assist the autoencoder in extracting abstract representation, and then a predictor estimates the RUL based on the abstract representation learned by the autoencoder. AA-LSTM is an end-to-end model, which jointly optimizes autoencoder, generative adversarial network, and predictor. This training mechanism improves the model's feature extraction and prediction capabilities for time series. We validate AA-LSTM on turbine engine datasets, and its performance outperforms state-of-the-art methods, especially on datasets with complex working conditions.

关键词： Remaining useful life Time series adversarial autoencoder Feature extraction

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