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WGAN-GP_Glu: A semi-supervised model based on double generator-Wasserstein GAN with gradient penalty algorithm for glutarylation site identification

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Computers in Biology and Medicine 2025年 184卷 109328-109328页

作者： Ning, Qiao Qi, Zedong Information Science and Technology Dalian Maritime University Liaoning Dalian China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Neusoft Education Technology Group Dalian China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

As an important post-translational modification, glutarylation plays a crucial role in a variety of cellular functions. Recently, diverse computational methods for glutarylation site identification have been proposed. However, the class imbalance problem due to data noise and uncertainty of non-glutarylation sites remains a great challenge. In this article, we propose a novel semi-supervised learning algorithm, called WGAN-GP_Glu, for identifying reliable non-glutarylation lysine sites from those without glutarylation annotation. WGAN-GP_Glu method is a multi-module framework algorithm, which mainly includes a reliable negative sample selection module, a deep feature extraction module, and a glutarylation site prediction module. In reliable negative sample selection module, we design an improved method of Wasserstein GAN with Gradient Penalty (WGAN-GP), named ReliableWGAN-GP, including three parts, two generators G1, G2 and a discriminator D, which can select reliable non-glutarylation samples from a great number of unlabeled samples. Generator G1 is utilized to generate noise data from unlabeled samples. For generator G2, both the positive sample and the noise data are used as inputs to improve the discriminant capability of discriminator D. Then, convolutional neural network and bidirectional long short-term memory network combined with attention mechanism are utilized to extract deep features for glutarylation samples and reliable non-glutarylation samples. Finally, a glutarylation site prediction module based on the three-layer fully connected layer is designed to make class predictions for samples. The sensitivity, specificity, accuracy and Matthew correlation coefficient of WGAN-GP_Glu on the independent test data set reach 90.58 %, 95.82 %, 94.44 % and 0.8645, respectively, which surpassed the existing methods for glutarylation sites prediction. Therefore, WGAN-GP_Glu can serve as a powerful tool in identifying glutarylation sites and the ReliableWGAN-GP algo

关键词： Gallium nitride

GraCEImpute: A novel graph clustering autoencoder approach for imputation of single-cell RNA-seq data

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Computers in Biology and Medicine 2025年 184卷 109400-109400页

作者： Wang, Yueying Li, Kewei Zhang, Ruochi Fan, Yusi Huang, Lan Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://***/supp/. © 2024 Elsevier Ltd

关键词： Gene expression

Structural Optimization of Causal Driven Model Based on Bayesian Network in High-dimensional Data Classification

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Applied Mathematics and Nonlinear Sciences 2025年第1期10卷

作者： Li, Kuo Wang, Aimin Wang, Limin Zhao, Yuetan Zhu, Xinyu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China

The Bayesian network (BN) model, as a big data graph model that integrates causal inference and probabilistic representation, has received widespread attention and research in both academia and industry. However, with the advent of the big data era, traditional BN structure learning algorithms have encountered unprecedented challenges in processing high-dimensional data, mainly manifested as a sharp increase in computational complexity and difficulty in achieving ideal accuracy requirements within an acceptable time range, which greatly limits their breadth and depth in practical applications. In response to this bottleneck problem, this article innovatively proposes a new approach that combines width learning theory with BN, referred to as Broad Bayesian Neural Network (Broad-BNN). This model effectively reduces the dimensionality of the original high-dimensional data by introducing a feature mapping layer and gradually expanding it, while achieving non-linear transformation of information and effective feature extraction. The experimental results show that the model proposed in this paper has achieved significant performance improvement in high-dimensional data classification problems, not only accelerating training speed but also significantly improving classification accuracy, providing a new perspective and solution for solving the difficulties of high-dimensional data processing. © 2025 Kuo Li et al., published by Sciendo.

关键词： Bayesian networks

SMT(LIA) Sampling with High Diversity

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： Software testing

Mask2Edge: Masking dependencies and dynamically capturing pixel differences in edge detection

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Expert Systems with Applications 2025年 286卷

作者： Zhou, Jianhang Zhao, Hongwei Qu, Daikun Xing, Long College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Army Artillery and Air Defense Academy Sergeant School Liaoning Shenyang110000 China

Edge detection plays an important role in computer vision tasks. Deep learning-based edge detectors commonly rely on encoding the long and short-term dependencies of pixel values to mine contextual information. They strongly focus on all positions in the image, ignoring the potential issue of over-encoding. Furthermore, most of these models have not attempted to leverage the inherent properties of edges. In this paper, we introduce a query-based edge detector named Mask2Edge, which is capable of masking dependencies and dynamically capturing pixel differences. Specifically, we first devise a masking strategy based on the sparsity of edges to alleviate the over-encoding issue. We propose a Region-guided Masked Attention, which adapts to edge detection and is capable of constraining cross-attention with appropriate masking intensity to extract relatively complete local features. Subsequently, we design a structure to capture the pixel differences that can help identify edges. We introduce dynamic convolutions into edge detection and refine the application scope and generation method of attention weights to effectively perceive changes in pixel gradients. Extensive experiments demonstrate the superiority of Mask2Edge compared with state-of-the-art methods. The source code is available at https://***/zhoujh2020/Mask2Edge. © 2025 Elsevier Ltd

关键词： Edge detection

NON-RADIATING ELASTIC SOURCES IN INHOMOGENEOUS ELASTIC MEDIA AT CORNERS WITH APPLICATIONS

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arXiv 2025年

作者： Diao, Huaian Geng, Yueran Tang, Ruixiang School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Mathematics Jilin University Changchun130012 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong

This paper is concerned with non-radiating elastic sources in inhomogeneous elastic media. We demonstrate that the value of non-radiating elastic sources must vanish at convex corners of their support, provided the sources exhibit Hölder continuous regularity near the corner. Additionally, their gradient must satisfy intricate algebraic relationships with the angles defining the underlying corners, assuming the sources have C1,α regularity with α ∈ (0, 1) in the neighborhood of the corners. To perform microlocal analysis around the corners, we employ the so-called complex geometrical optics (CGO) solutions as test functions within a partial differential system. These characterizations of non-radiating elastic sources in inhomogeneous elastic media at corners enable us to establish the unique identifiability results for determining the position and shape of radiating elastic sources by a single far-field measurement, both locally and globally. The uniqueness result by a single far-field measurement is a challenging problem with a colorful history in inverse scattering. Indeed, when the support of a radiating elastic source is a convex polygon, we can simultaneously determine the shape of the elastic source and its values at the corners, provided the source is Hölder continuous at the corner. Furthermore, when the source function exhibits C1,α regularity in the neighborhood of a corner, the gradient of the source function at that corner can also be generically *** Codes 35Q74, 35R30, 74B05, 86A22 © 2025, CC BY.

关键词： Geometrical optics

Incomplete Graph Learning: A Comprehensive Survey

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arXiv 2025年

作者： Xia, Riting Liu, Huibo Li, Anchen Liu, Xueyan Zhang, Yan Zhang, Chunxu Yang, Bo College of Computer Science Inner Mongolia University Hohhot010021 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.1 Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

Generative Classifier for Domain Generalization

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arXiv 2025年

作者： Long, Shaocong Zhou, Qianyu Li, Xiangtai Ying, Chenhao Tong, Yunhai Ma, Lizhuang Luo, Yuan Tao, Dacheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Nanyang Technological University Singapore Peking University Beijing China

Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods predominantly focus on learning domain invariance across domains, however, such methods overlook the untapped potential inherent in domain-specific information. While the prevailing practice of discriminative linear classifier has been tailored to domain-invariant features, it struggles when confronted with diverse domain-specific information, e.g., intra-class shifts, that exhibits multi-modality. To address these issues, we explore the theoretical implications of relying on domain-invariant features, revealing the crucial role of domain-specific information in mitigating the target risk for DG. Drawing from these insights, we propose Generative Classifier-driven Domain Generalization (GCDG), introducing a generative paradigm for the DG classifier based on Gaussian Mixture Models (GMMs) for each class across domains. GCDG consists of three key modules: Heterogeneity Learning Classifier (HLC), Spurious Correlation Blocking (SCB), and Diverse Component Balancing (DCB). Concretely, HLC attempts to model the feature distributions and thereby capture valuable domain-specific information via GMMs. SCB identifies the neural units containing spurious correlations and perturbs them, mitigating the risk of HLC learning irrelevant spurious patterns. Meanwhile, DCB ensures a balanced contribution of components within HLC, preventing the underestimation or neglect of critical components. In this way, GCDG excels in capturing the nuances of domain-specific information characterized by diverse distributions. GCDG demonstrates the potential to reduce the target risk and encourage flat minima, improving the model’s generalizability. Extensive experiments show GCDG’s comparable performance on five DG benchmarks and one face anti-spoofing dataset, seamlessly integrating into existing DG methods with consistent improvements. Code will be avail

关键词： Discriminant analysis

Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Song, Jincai Chen, Haipeng Lyu, Yingda Nie, Weizhi Liu, An-An Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China Jilin University Public Computer Education and Research Center Jilin Changchun130012 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China

Deep learning performance may decrease substantially with unseen heterogeneous data. While most unsupervised domain adaptation (UDA) methods seek to address this through image alignment, they often ignore uncertainty style fluctuations within the target domain. When testing image styles vary in both direction and intensity, such models may fail to adapt. Furthermore, existing UDA methods tend to over-reliance on domain-level entire feature alignment, resulting in potentially over-exploiting semantic content-independent cues (e.g., intensity) as shortcut features. To address these limitations, this paper introduces an innovative and model-agnostic Causality-inspired Representation Learning Based on Target Style Imitation method for UDA. Specifically, we propose a novel Target Style Imitation (TSI) data augmentation approach to diversify the training data and align training and unseen target testing image styles. TSI constructs a Gaussian distribution for the target domain style and simulates unseen testing style variations through random sampling. Additionally, inspired by the stable and generalizable causal mechanism, we propose Causality-inspired Representation Learning (CRL) based on TSI method to enforce feature representations to adhere to causal properties (i.e., Separation and Independence) essential for robust UDA, thus fostering the model to focus on the domain-invariant semantic features. Our method surpasses state-of-the-art methods on two cross-modality medical image segmentation datasets. © 1991-2012 IEEE.

关键词： Semantic Segmentation