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

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

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Neural Explainable Recommender Model Based on Attributes and Reviews

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Journal of Computer Science & Technology 2020年第6期35卷 1446-1460页

作者： Yu-Yao Liu Bo Yang Hong-Bin Pei Jing Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China

Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, resulting in good performance, they cannot provide the reason for their recommendations. Existing explainable recommender methods can be mainly divided into two types. The first type models highlight reviews written by users to provide an explanation. For the second type, attribute information is taken into consideration. These approaches only consider one aspect and do not make the best use of the existing information. In this paper, we propose a novel neural explainable recommender model based on attributes and reviews (NERAR) for recommendation that combines the processing of attribute features and review features. We employ a tree-based model to extract and learn attribute features from auxiliary information, and then we use a time-aware gated recurrent unit (T-GRU) to model user review features and process item review features based on a convolutional neural network (CNN). Extensive experiments on Amazon datasets demonstrate that our model outperforms the state-of-the-art recommendation models in accuracy of recommendations. The presented examples also show that our model can offer more reasonable explanations. Crowd-sourcing based evaluations are conducted to verify our model's superiority in explainability.

关键词： recommender system explainable recommendation review usefulness attribute usefulness

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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

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665468190

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods. © 2022 IEEE.

关键词： Forecasting

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Attribute imputation autoencoders for attribute-missing graphs

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knowledge-Based Systems 2024年 291卷

作者： Xia, Riting Zhang, Chunxu Li, Anchen Liu, Xueyan Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China

Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data imputation techniques based on graph autoencoders are commonly used for attribute-missing graphs. However, this method cannot effectively integrate existing attributes and structural information during the encoding stage and is prone to introducing noise, resulting in inaccurate imputation. In addition, the expressiveness of decoders in existing methods is limited because their network architecture has not been adequately designed, which restricts the accuracy and robustness of the generated attributes. To address these issues, we propose a novel Attribute Imputation AutoEncoder for attribute-missing graphs, named AIAE. In particular, during the encoding stage, a dual encoder based on knowledge distillation is designed to encode both attribute and structural information into representations of attribute-missing nodes to achieve more accurate imputation. To avoid introducing noise, we fully exploit the observed information by reorganizing the representations of the attribute-missing and attribute-observed nodes. In the decoding stage, we propose a multi-scale decoder with masking to make the decoder more expressive and enhance its robustness and generative ability. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods in attribute-missing graphs. © 2024 Elsevier B.V.

关键词： Topology

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SMT(LIA) Sampling with High Diversity

arXiv

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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

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UAV Deployment Optimization in UAV-assisted Wireless Communications

arXiv

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arXiv 2024年

作者： Zhang, Xueqi Wang, Aimin Sun, Geng Liu, Lingling Zhang, Jing College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Communication Engineering Jilin University Changchun130012 China

Due to the fact that the locations of base stations (BSs) cannot be changed after they are installed, it is very difficult to communicate directly with remote user equipment (UE), which will directly affect the lifespan of the system. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for fifth-generation wireless communications due to the flexible and cost-effective deployment. However, with the limited onboard energy of UAV and slow progress in energy storage technology, it is a key challenge to achieve the energy-efficient communication. Therefore, in this article, we study a wireless communication network using a UAV as a high-altitude relay, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of system by optimizing the deployment of UAV, including the locations and number of UAV hover points. Since the formulated URDOP is a mixed-integer programming problem, it presents a significant challenge for conventional gradient-based approaches. To this end, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the formulated URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms across multiple instances. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bai, Tian Li, Chu Peng, Xinyue Guan, Haotian Zhang, Zefan Wang, Guishen Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Jilin University School of Nursing Changchun China Jilin University School of Life Sciences Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy. © 2023 IEEE.

关键词： drug-drug interaction prediction graph-level classification line graph line subgraph strategy node feature enhancing method

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CCDD: A Tractable Representation for Model Counting and Uniform Sampling

arXiv

引用

arXiv 2022年

作者： Lai, Yong Meel, Kuldeep S. Yap, Roland H.C. Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computing National University of Singapore Singapore119077 Singapore

knowledge compilation concerns with the compilation of representation languages to target languages supporting a wide range of tractable operations arising from diverse areas of computer science. Tractable target compilation languages are usually achieved by restrictions on the internal nodes (∧ or ∨) of the NNF. In this paper, we propose a new representation language CCDD, which introduces new restrictions on conjunction nodes to capture equivalent literals. We show that CCDD supports two key queries, model counting and uniform samping, in polytime. We present algorithms and a compiler to compile propositional formulas expressed in CNF into CCDD. Experiments over a large set of benchmarks show that our compilation times are better with smaller representations than state-of-art Decision-DNNF, SDD and OBDD[∧] compilers. We apply our techniques to model counting and uniform sampling, and develop model counter and uniform sampler on CNF. Our empirical evaluation demonstrates the following significant improvements: our model counter can solve 885 instances while the prior state of the art solved only 843 instances, representing an improvement of 43 instances;and our uniform sampler can solve 780 instances while the prior state of the art solved only 648 instances, representing an improvement of 132 instances. Copyright © 2022, The Authors. All rights reserved.

关键词： Program compilers

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Towards More Accurate and Complete Iris Segmentation Using Hybrid Transformer U-Net

Towards More Accurate and Complete Iris Segmentation Using H...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ye Sun Yinan Lu Yuanning Liu Xiaodong Zhu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

ISBN: (纸本)9781665463959

Iris images captured in less-constrained environments often suffer from adverse noise, challenging many existing segmentation algorithms. In this paper, we propose an efficient Hybrid Transformer U-Net (HTU-Net) to address this dilemma. Unlike previous studies that only focus on utilizing popular CNN technology to predict iris masks accurately, HTU-Net can simultaneously obtain segmentation masks and parameterized pupillary and limbic boundaries by a multi-task network, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We explore the application of the Transformer in iris segmentation and propose a hybrid encoder that employs convolutional layers to extract local intensity features and the Transformer to capture long-range associative information. For decoding, we adopt a novel Multi-Head Dilated Attention to exploit the multi-scale contextual information by gating mechanism, thus emphasizing the important features and rendering powerful representations. Inspired by the consistent class characteristics of iris, we further devise a Pyramid Center-Aware Module to capture the global structural context of iris from a categorical perspective to improve performance. Experimental results show that our method, with fewer parameters than previous approaches, achieves competitive or new state-of-the-art performance in both iris segmentation and localization on three challenging iris datasets. Code will be released at https://***/Syloveslife/HTU-Net.

关键词： Image segmentation Biological system modeling Transformers Feature extraction Rendering (computer graphics) Prediction algorithms Multitasking

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