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VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025年 261卷 108605-108605页

作者： Rollo, Cesare Pancotti, Corrado Sartori, Flavio Caranzano, Isabella D'Amico, Saverio Carota, Luciana Casadei, Francesco Birolo, Giovanni Lanino, Luca Sauta, Elisabetta Asti, Gianluca Buizza, Alessandro Delleani, Mattia Zazzetti, Elena Bicchieri, Marilena Maggioni, Giulia Fenaux, Pierre Platzbecker, Uwe Diez-Campelo, Maria Haferlach, Torsten Castellani, Gastone Porta, Matteo Giovanni Della Fariselli, Piero Sanavia, Tiziana Univ Torino Computat Biomed Unit Dept Med Sci Via Santena 19 I-10126 Turin Italy IRCCS Humanitas Res Hosp Via Manzoni 56 I-20089 Rozzano Milan Italy Train Srl via Alessandro Manzoni 56 I-20089 Rozzano Milan Italy Univ Bologna Dept Med & Surg Sci DIMEC I-40126 Bologna Italy IRCCS Ist Sci Neurol Bologna I-40138 Bologna Italy Hop St Louis Hematol Bone Marrow Transplantat Paris France Univ Hosp Leipzig Med Clin & Policlin 1 Hematol & Cellular Therapy Leipzig Germany Hosp Univ Salamanca Hematol Dept Salamanca Spain MLL Munich Leukemia Lab Max Lebsche Pl 31 Munich Germany IRCCS Azienda Osped Univ Bologna S Orsola I-40138 Bologna Italy Humanitas Univ Dept Biomed Sci Via Montalcini 4 I-20072 Pieve Emanuele Milan Italy

Background and Objectives Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality. Methods We introduce VAE-Surv, a multimodal computational framework for patients' stratification and prognosis prediction. VAE-Surv integrates a variational autoencoder (VAE), which reduces the high-dimensional space characterizing the molecular data, with a deep survival model, which combines the embedded information with the clinical features. The VAE embedding step prioritizes local coherence within the feature space to detect potential nonlinear relationships among the molecular markers. The latent representation is then exploited to perform K-means clustering. To test the clinical robustness of the algorithm, VAE-Surv was applied to the Genomed4all cohort of Myelodysplastic Syndromes (MDS), comparing the identified subtypes with the World Health Organization (WHO) classification. The survival outcome was compared with the state-of-the-art Cox model and its penalized versions. Finally, to assess the generalizability of the results, the method was also validated on an external MDS cohort. Results Tested on 2,043 patients in the GenomMed4All cohort, VAE-Surv achieved a median C-index of 0.78, outperforming classical approaches. In addition, the latent space enhanced the clustering performance compared to a traditional approach that applies the clustering directly to the input data. Compared to the WHO 2016 MDS subtypes, the analysis of the identified clusters showed that the proposed framework can capture existing clinical categorizations while also sug

关键词： Survival analysis Deep Learning variational autoencoder Myelodysplastic syndrome Genetic-based clustering

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Advancing Agile Software Cost Estimation Through Data Synthesis: A Comparative Analysis of Five Generation Techniques

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IEEE ACCESS 2025年 13卷 63219-63236页

作者： Zhao, Xiaoyan Mansor, Zulkefli Razali, Rozilawati Nazri, Mohd Zakree Ahmad Xiong, Xin Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Ctr Software Technol & Management Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

Agile has been used in software development for over 20 years and is the preferred development method for more than 85% of software companies. However, cost estimation in agile development remains a significant challenge. This is reflected in the fact that the accuracy of estimation still needs improvement, and most cost estimation techniques still rely on the team's experience and knowledge. While machine learning algorithms have performed better in this area, the lack of sufficient agile cost data hinders large-scale training and in-depth research. To address this issue, this study selected five data generation techniques-variational autoencoder (VAE), Wasserstein Generative Adversarial Network (WGAN), Synthetic Minority Over-sampling Technique for Nominal and Continuous Features (SMOTE-NC), Data Augmentation for Tabular Data (Augmentation), and Tabular Data Diffusion Probabilistic Models (TabDDPM)-based on the characteristics of agile cost data. Using cost data from 75 agile projects, these techniques were employed to generate three sets of data with sizes of 200, 500, and 1000. A performance evaluation model was created based on consistency, authenticity, diversity, and effectiveness to verify the performance of these generated data. The experimental results show that WGAN consistently scored 16 out of 20 points across all three data sets, excelling in data consistency and authenticity. SMOTE-NC and Augmentation Were followed. SMOTE-NC scored 15 out of 20 points for all data sizes and performed best in terms of effectiveness, with an MMRE of 88.16% and a PRED (0.2) of 84.5%. Augmentation performed the best when generating 1000 data points. These findings highlight the potential of data generation technologies, particularly WGAN, in enhancing agile cost estimation and providing guidance on selecting the appropriate amount of data. This lays a foundation for further development of machine learning algorithms in this field and offers valuable insights for other res

关键词： Costs Estimation Generative adversarial networks Training Agile software development Data models Hidden Markov models Data collection Data augmentation autoencoders Data generation technique software cost estimation agile software development variational autoencoder Wasserstein generative adversarial network synthetic minority over-sampling technique for nominal and continuous features data augmentation for tabular data tabular data diffusion probabilistic model

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A novel user preference-aware content caching algorithm in mobile edge networks

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12273-12296页

作者： Firouzjaee, Mostafa Taghizade Jamshidi, Kamal Moghim, Neda Univ Isfahan Fac Comp Engn Esfahan *** Iran Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

One of the most important strategies used to mitigate the adverse impacts of traffic growth on mobile networks is caching. By caching at the edge, the backhaul traffic load is reduced, and the quality of service for the user is increased. Developing an effective caching algorithm requires accurate prediction of the future popularity of the content, which is a challenging issue. In recent years, deep learning models have achieved high predictive accuracy due to advancements in data availability and increased computing power. In this paper, we present a caching algorithm called the user preference-aware content caching algorithm (UPACA). This algorithm is specifically designed for an edge content delivery platform where users can access content services provided by a remote content provider. UPACA operates in two steps. In the first step, the proposed collaborative filtering-based popularity prediction algorithm (CFPA) is used to predict future content popularities. CFPA utilizes a gated residual variational autoencoder collaborative filtering model to predict users' future preferences and calculate the future popularity of content. This algorithm considers the popularity of the content as well as the number and timing of content requests. Experimental results demonstrate that UPACA outperforms previous methods in terms of cache hit rates and user utilities.

关键词： Content caching Popularity prediction Deep learning variational autoencoder Collaborative filtering

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Characterizing Marine Magnetic Anomalies: A Machine Learning Approach to Advancing the Understanding of Oceanic Crust Formation

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2025年第2期130卷

作者： Wu, S. Thoram, S. Sun, J. Sager, W. W. Chen, J. Univ Houston Dept Earth & Atmospher Sci Houston TX 77004 USA Univ Houston Dept Elect & Comp Engn Houston TX USA

Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)-based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi-automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user-specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid-Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data-driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.

关键词： marine magnetic anomalies machine learning linear magnetic anomaly oceanic crust formation variational autoencoder

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Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9026-9040页

作者： Fu, Kaicheng Du, Changde Wang, Shengpei He, Huiguang Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100190 Peoples R China

Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans;the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.

关键词： Decoding Brain modeling Functional magnetic resonance imaging Predictive models Emotion recognition Dimensionality reduction Pattern recognition Fine-grained emotion decoding multi-label learning multi-view learning product of experts (PoEs) variational autoencoder

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Light-Aware Contrastive Learning for Low-Light Image Enhancement

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第9期20卷 1-24页

作者： Wu, Xu Lai, Zhihui Zhou, Jie Hou, Xianxu Pedrycz, Witold Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Xian Jiaotong Liverpool Univ Sch AI & Adv Comp Suzhou Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Univ Nottingham Ningbo China Dept Comp Sci Ningbo Peoples R China

Low-Light Image Enhancement (LLIE) presents challenges due to texture information loss and uneven illumination, which can distort feature distribution and reduce the quality of the enhanced images. However, current deep learning methods for LLIE only use supervised information from clear images to extract low-light image features, while disregarding the negative information in low-light images (i.e., low illumination and noise). To address these challenges, we propose a novel LLIE method, LACR-VAE, by leveraging the negative information and considering the uneven illumination. In particular, a Light-Aware Contrastive Regularization (LACR) based on contrastive learning is designed to exploit information from both clear and low-light images. The LACR aims to align latent variables of enhanced images with clear images, away from those of low-light images. This allows the method to prioritize essential elements for LLIE and minimize noise and lighting variations. Furthermore, considering the uneven illumination with diverse region sizes and shapes, a Region-CAlibrated Module (RCAM) is present to learn local and global illumination relations among image regions, and an Attention-guided Multi-Scale Module (AMSM) is designed to extract multi-scale features that improve the model's representation capability. Extensive experiments show that our method achieves superior performance than previous works. Specifically, our method yields a significant enhancement in the National Aeronautics and Space Administration (NASA) testset, achieving an improvement of at least 0.99 in PSNR and 0.0409 in SSIM. Codes and datasets are available at https://***/csxuwu/LACR-VAE.

关键词： Low-light image enhancement variational autoencoder contrastive learning transformer

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Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Liu, Renjing Qian, Kun He, Xinwei Li, Hongwei China Univ Geosci Wuhan Sch Math & Phys Wuhan 430074 Peoples R China

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular *** expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific *** is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

关键词： ScRNA-seq Cellular heterogeneity Integration variational autoencoder Disentangled representation learning Condition domain adaptation

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A Novel CVAE-Based Sequential Monte Carlo Framework for Dynamic Soft Sensor Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第3期20卷 3789-3800页

作者： Sun, Wenxin Xiong, Weili Chen, Hongtian Chiplunkar, Ranjith Huang, Biao Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

In industrial processes, quality variables are typically sampled at a considerably lower frequency than system inputs due to technical or cost constraints. Dynamic soft sensors utilize temporal prediction to bridge these sampling gaps, thus enabling real-time closed-loop control. However, existing approaches primarily focus on one-step prediction accuracy, potentially leading to significant deviations in long-term predictions. In addition, these methods are incapable of evaluating the reliability of prediction results, subsequently increasing the potential risk of closed-loop systems. To tackle these challenges, this study presents a novel regression modeling approach based on the conditional variational autoencoder (CVAE) framework. In contrast to traditional regression approaches, this method focuses on modeling the transition probability distribution of the system, allowing the model to produce a range of credible quality variable predictions via Monte Carlo (MC) sampling. Based on the CVAEs, the sequential MC method is further employed to simulate diverse potential system state trajectories, thereby achieving multistep soft measurement prediction. Compared with traditional soft measurement techniques, the proposed method demonstrates lower prediction biases and the capacity to assess the credibility of prediction results from a probabilistic standpoint. When online quality variables are assessed by the laboratory, this method can update predictions utilizing the resampling scheme. Two case studies are offered to validate the effectiveness of the proposed scheme.

关键词： Predictive models Real-time systems Adaptation models Time measurement Soft sensors Monte Carlo methods Estimation Dynamic soft sensor (SS) prediction calibration sequential Monte Carlo (SMC) variational autoencoder

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Stego-Image Synthesis Employing Data-Driven Continuous Variable Representations of Cover Images

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IEEE ACCESS 2024年 12卷 146749-146770页

作者： Almuayqil, Saleh Naif Fadel, Magdy M. Hassan, Mohammed K. Hagras, Esam A. A. Said, Wael Jouf Univ Coll Comp & Informat Sci Informat Syst Dept Sakaka 72314 Saudi Arabia Mansoura Univ Fac Engn Comp Engn & Syst Dept Mansoura 35516 Egypt Horus Univ EGYPT HUE Fac Engn Mechatron Dept New Damietta 34517 Egypt Delta Univ Sci & Technol Fac Engn Elect & Commun Dept Gamasa 35712 Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept Zagazig 44511 Egypt Taibah Univ Coll Comp Sci & Engn Comp Sci Dept Madinah 42353 Saudi Arabia

The security of stego-images is a crucial foundation for analyzing steganography algorithms. Recently, steganography has made significant strides in ongoing conflicts with steganalysis. In order to increase the security of stego-images, steganography must be able to evade detection using steganalysis methods. Secret information is typically hidden using traditional embedding-based steganography, which inevitably leaves traces of the modifications that can be found using more sophisticated machine-learning-based steganalysis techniques. Steganography without embedding (SWE) outperforms machine-learning-based steganalysis techniques because it does not require alteration of the data of the cover image. A novel image SWE method based on deep convolutional generative adversarial networks (GANs) is proposed to synthesize stego-images led by embedded text. The variational autoencoder (VAE) in the GAN model is utilized to synthesize the stego-image, based on interpolating the secret text in a continuous variable representation of the cover image. To further improve the framework's performance and shorten processing times, the whale optimization algorithm (WOA) is used to identify the optimal VAE structure. When creating a stego-image, no embedding or modification procedures are required, and after training, a different convolutional neural network (CNN) known as the extractor can correctly extract the data from the image. The experimental results revealed that this approach has the advantages of evading detection using innovative deep learning (DL) steganalysis architecture and accurate information extraction.

关键词： Convolutional neural networks Convolutional neural network data hiding generative adversarial network generative artificial intelligence generative image synthesis steganography variational autoencoder Convolutional neural networks Convolutional neural network data hiding generative adversarial network generative artificial intelligence generative image synthesis steganography variational autoencoder

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Bearing degradation prediction based on deep latent variable state space model with differential transformation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 220卷

作者： Ran, Bi Peng, Yizhen Wang, Yu Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Xi An Jiao Tong Univ Coll Mech Engn Xian 710000 Peoples R China

Rolling bearings are a critical component of mechanical transmission equipment. Predicting their degradation trend is crucial for ensuring safe and stable equipment operation. Most existing bearing degradation prediction methods based on state space models (SSMs) use either linear functions or limited nonlinear functions (e.g., exponential/power laws) to construct the state and measurement equations. As such, these models fail to adapt to the complex and varied nonlinear degradation processes that occur in real-world environments. To address this limitation, we developed a deep latent variable-driven state space degradation model and employed it for bearing degradation prediction. Owing to the powerful nonlinear modeling ability of deep learning models, the proposed method extends the applicability of state space equations. In addition, it inherits the advantages of SSMs and can model uncertainties in a structured manner. Furthermore, the model was integrated with differential pre-transformation to improve its longterm prediction performance. Finally, to validate the effectiveness of the proposed model in predicting bearing degradation, experiments were conducted using a bearing dataset from the PRONOSTIA platform and real wind turbine bearing data. Results showed that the proposed method yielded higher bearing degradation prediction accuracy than existing methods, thus demonstrating the superior performance of the proposed model in predicting bearing degradation.

关键词： Bearings Degradation prediction variational autoencoder State space model

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