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Juan Wang

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25 papers

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EAAI Journal 2026 Journal Article

A computer vision-based approach for detecting tenon pull-out damage in ancient timber structures

Juan Wang
Yuan Yao
Yujing Yuan
Lei Tan
Xiaohui Yang
Na Yang

Ancient timber structures featuring mortise-tenon joints are vital cultural heritage forms in China and East Asia. Accurate and swift detection of joint pull-out is essential for ensuring structural safety and stability, thereby protecting this priceless heritage. Traditional tenon pull-out damage detection methods grapple with issues like dataset scarcity, complex backgrounds, wood grain interference, and a lack of suitable quantification methods. Considering the critical role of mortise-tenon connections in ancient timber structures’ structural integrity, adopting modern, non-destructive damage identification techniques is crucial. Computer vision, as a non-destructive approach, is especially apt for this purpose. To tackle these challenges, a computer vision-based method is proposed for precisely locating and quantifying tenon pull-out damage in ancient timber structures. A comprehensive detection process is established, including dataset construction for accuracy validation, damage location identification, and quantification of pull-out extent. Comparative analysis with instrument measurements reveals high recognition accuracy of the method. This approach offers a reference for assessing the condition of mortise-tenon joints in ancient timber structures, significantly aiding in the scientific preservation of cultural heritage.

JBHI Journal 2026 Journal Article

A Hierarchical Attention-Based Negative Sampling Method for Drug Repositioning Using Neighborhood Interaction Fusion

Chenglong Mi
Ling-Yun Dai
Junliang Shang
Rong Zhu
Juan Wang
Feng Li

Accurate prediction of drug–disease associations (DDAs) is essential for drug repositioning and the development of novel therapeutic strategies. However, existing methods often suffer from limited prior knowledge and the use of oversimplified negative sampling techniques, which hinder their ability to capture the complex relationships between drugs and diseases. To break through these limitations, we propose a new model, Hierarchical Attention Mechanism-Based Negative Sampling (HA-NegS), which aims to enhance the prediction of potential DDAs. In this study, HA-NegS further computes the similarity information between drugs and diseases and constructs heterogeneous and homogeneous networks based on it. For the similarity network, HA-NegS fuses Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to effectively capture the neighborhood features of the target nodes. Subsequently, the model incorporates a hierarchical sampling strategy using the PageRank algorithm to rank nodes in descending order of global importance. The attention mechanism is then used to calculate the attention score and re-rank the nodes accordingly. This approach ensures the reliability of the negative sample selection. In order to obtain optimized representations, we use graph contrastive learning methods to refine drug and disease features with homogeneous and heterogeneous neighborhood information. Experimental results on a benchmark dataset show that HA-NegS outperforms existing baseline methods in predicting DDA. In addition, case studies for Alzheimer’s disease and Parkinson’s disease highlight the effectiveness of HA-NegS in discovering new therapeutic applications for existing drugs.

JBHI Journal 2026 Journal Article

Cluster-Guided Contrastive Learning With Masked Autoencoder for Spatial Domain Identification Based on Spatial Transcriptomics

Juan Wang
Qi Gao
Shasha Yuan
Junliang Shang

Recent advancements in spatial transcriptomics technology have enabled the capture of gene expression profiles while maintaining spatial information. Accurately identifying spatial clustering plays a pivotal role in analyzing spatial transcriptomics data and understanding tissue microenvironments. However, current spatial domain identification methods cannot explore the complex relationship of gene expression profiles and spatial topology. To alleviate this issue, we propose STMCCL, a novel self-supervised learning framework that jointly trains a masked autoencoder and cluster-guided contrastive learning. This framework extracts informative latent representations from gene expression profiles and spatial information. Specifically, we first use data augmentation strategies to build augmented views and employ a masked encoder to generate a feature view. Then, encoders are applied to learn view-unique embeddings of each view. Furthermore, we introduce a multiple cluster-perspectives module that considers both geometric and structural relationships between clusters to produce more reliable cluster assignments. Finally, to derive more discriminative positives and negatives, the cluster-guided contrastive module calculates the confidence of each sample based on the initial cluster. Comprehensive experiments on 7 public datasets demonstrate that STMCCL outperforms the state-of-the-art baselines with finer-scale spatial domain identification.

JBHI Journal 2025 Journal Article

HSC-T: B-Ultrasound-to-Elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis

Hongcheng Han
Zhiqiang Tian
Qinbo Guo
Jue Jiang
Shaoyi Du
Juan Wang

Elastography ultrasound imaging is increasingly important in the diagnosis of thyroid cancer and other diseases, but its reliance on specialized equipment and techniques limits widespread adoption. This paper proposes a novel multimodal ultrasound diagnostic pipeline that expands the application of elastography ultrasound by translating B-ultrasound (BUS) images into elastography images (EUS). Additionally, to address the limitations of existing image-to-image translation methods, which struggle to effectively model inter-sample variations and accurately capture regional-scale structural consistency, we propose a BUS-to-EUS translation method based on hierarchical structural consistency. By incorporating domain-level, sample-level, patch-level, and pixel-level constraints, our approach guides the model in learning a more precise mapping from BUS to EUS, thereby enhancing diagnostic accuracy. Experimental results demonstrate that the proposed method significantly improves the accuracy of BUS-to-EUS translation on the MTUSI dataset and that the generated elastography images enhance nodule diagnostic accuracy compared to solely using BUS images on the STUSI and the BUSI datasets. This advancement highlights the potential for broader application of elastography in clinical practice.

JBHI Journal 2025 Journal Article

Identifying Disease-Gene Associations by Topological and Biological Feature-based Data Augmentation and Graph Neural Networks

Yuan Zhang
Juan Wang
Jiajie Xing
Xiaomin Chen

Predicting gene-disease associations is essential for understanding disease pathogenesis and determining therapeutic targets. While prior methods have integrated diverse biological information to make predictions, they still encounter several challenges. First, incomplete and sparse gene-disease association data constrain model performance. Second, integrating heterogeneous data sources is not straightforward. To address these challenges, we propose a novel method, DAVGAE, which combines data augmentation, Variational Graph Auto-Encoders (VGAE), and attention mechanisms. DAVGAE integrates both the biological and topological features of genes and diseases to address challenges such as data sparsity and heterogeneity. By leveraging these features, it calculates cosine similarity scores for gene-disease pairs and applies a novel data augmentation strategy to enhance association data by selecting gene-disease associations with higher similarity scores. Using a four-layer Graph Neural Network (GNN) encoder, DAVGAE effectively learns robust and discriminative representations for genes and diseases within the association network. Finally, an inner product decoder predicts association scores for all gene-disease pairs. Comprehensive experiments on three gene-disease association datasets reveal that DAVGAE outperforms baseline models in predicting gene-disease associations.

YNIMG Journal 2024 Journal Article

Investigation of white matter functional networks in young smokers

Junxuan Wang
Ting Xue
Daining Song
Fang Dong
Yongxin Cheng
Juan Wang
Yuxin Ma
Mingze Zou

AIMS: This study investigated the changes in the organizational and intrinsical activities of the white matter functional networks (WMFNs) in young smokers using resting-state functional magnetic resonance imaging. METHODS: A data-driven approach was used to characterize the WMFNs of 30 young smokers and 30 non-smokers. We applied K-means clustering to the neuroimaging data to delineate the WMFNs. Functional neural activities of the WMFNs were compared between the two groups. Correlation analyses were also conducted for the WMFNs neural activities of and clinical indicators of smoking. RESULTS: Eight WMFNs were identified in both groups. Compared to non-smokers, young smokers demonstrated a different dorsal attention network and lack of a frontostriatal network. The neural activities in the frontal network, deep frontoparietal network, and visual network were reduced in young smokers. Further correlation analyses showed that the decreased neural activity in the deep frontal network and deep frontoparietal network were significantly negatively correlated with the Fagerström Test for Nicotine Dependence. CONCLUSION: Young smokers exhibited differences in the organizational structure and neural activity intensities of the WMFNs. The present findings may indicate the importance of WMFNs in young smokers, which can help in obtaining a comprehensive understanding of the neural mechanisms underlying smoking addiction.

JBHI Journal 2024 Journal Article

M 3 HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction

Shuang Wang
Jin-Xing Liu
Feng Li
Juan Wang
Ying-Lian Gao

Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases is critical. In this paper, a new microbe-disease association prediction model is proposed that combines a multi-view multi-modal network and a multi-scale feature fusion mechanism, called M 3 HOGAT. Firstly, a microbe-disease association network and multiple similarity views are constructed based on multi-source information. Then, consider that neighbor information from disparate orders might be more adept at learning node representations. Consequently, the higher-order graph attention network (HOGAT) is devised to aggregate neighbor information from disparate orders to extract microbe and disease features from different networks and views. Given that the embedding features of microbe and disease from different views possess varying importance, a multi-scale feature fusion mechanism is employed to learn their interaction information, thereby generating the final feature of microbes and diseases. Finally, an inner product decoder is used to reconstruct the microbe-disease association matrix. Compared with five state-of-the-art methods on the HMDAD and Disbiome datasets, the results of 5-fold cross-validations show that M 3 HOGAT achieves the best performance. Furthermore, case studies on asthma and obesity confirm the effectiveness of M 3 HOGAT in identifying potential disease-related microbes.

AAAI Conference 2024 Conference Paper

RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing

Xinyu Sun
Zhikun Zhao
Lili Wei
Congyan Lang
Mingxuan Cai
Longfei Han
Juan Wang
Bing Li

Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images, consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP parameters are manually tuned in isolation by imaging experts according to application-specific quality and performance metrics, which is time-consuming and biased towards human perception due to complex interaction with the output image. Since the relationship between any single parameter’s variation and the output performance metric is a complex, non-linear function, optimizing such a large number of ISP parameters is challenging. To address this challenge, we propose a novel Sequential ISP parameter optimization model, called the RL-SeqISP model, which utilizes deep reinforcement learning to jointly optimize all ISP parameters for a variety of imaging applications. Concretely, inspired by the sequential tuning process of human experts, the proposed model can progressively enhance image quality by seamlessly integrating information from both the image feature space and the parameter space. Furthermore, a dynamic parameter optimization module is introduced to avoid ISP parameters getting stuck into local optima, which is able to more effectively guarantee the optimal parameters resulting from the sequential learning strategy. These merits of the RL-SeqISP model as well as its high efficiency are substantiated by comprehensive experiments on a wide range of downstream tasks, including two visual analysis tasks (instance segmentation and object detection), and image quality assessment (IQA), as compared with representative methods both quantitatively and qualitatively. In particular, even using only 10% of the training data, our model outperforms other SOTA methods by an average of 7% mAP on two visual analysis tasks.

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JBHI Journal 2024 Journal Article

Spatiotemporal Network Based on GCN and BiGRU for Seizure Detection

Jie Xu
Shasha Yuan
Junliang Shang
Juan Wang
Kuiting Yan
Yankai Yang

As an important tool for detecting and diagnosing epilepsy, multi-channel EEG records the neuronal activities of different brain regions. Visual identification of abnormal EEG signals poses challenges, making the use of artificial intelligence techniques for automated seizure detection an inevitable trend. However, existing seizure detection methods often overlook the spatial relationship between EEG channels, which can't take full advantage of brain network structure. In this paper, we design an end-to-end spatiotemporal architecture for seizure detection based on Graph Convolutional Networks (GCN) and Bidirectional Gated Recurrent Units (BiGRU) to efficiently model the spatial dependence and temporal dynamics of EEG. Firstly, the original EEG signals are preprocessed by applying wavelet transform for temporal-frequency analysis. The Pearson correlation matrix is computed for specific frequency bands and GCN is utilized to extract spatial features between EEG channels. Then, these features are sent into the BiGRU network to capture temporal relationships. Finally, the detection decisions are achieved using fully connected layers and the multi-level decision rules are implemented to provide the final results. The proposed method is validated on CHB-MIT EEG dataset, achieving 98. 85% sensitivity, 95. 83% specificity, 97. 35% accuracy, 97. 4% F1-score, and 97. 33% AUC. This network fusions multiple EEG characteristics in the spatial-temporal-frequency domains to improve the detection performance and the promising result demonstrates that the performance of this model is superior to or on par with existing methods.

JBHI Journal 2023 Journal Article

A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis

Tian-Jing Qiao
Jin-Xing Liu
Junliang Shang
Shasha Yuan
Chun-Hou Zheng
Juan Wang

Single-cell RNA sequencing (scRNA-seq) technology can provide expression profile of single cells, which propels biological research into a new chapter. Clustering individual cells based on their transcriptome is a critical objective of scRNA-seq data analysis. However, the high-dimensional, sparse and noisy nature of scRNA-seq data pose a challenge to single-cell clustering. Therefore, it is urgent to develop a clustering method targeting scRNA-seq data characteristics. Due to its powerful subspace learning capability and robustness to noise, the subspace segmentation method based on low-rank representation (LRR) is broadly used in clustering researches and achieves satisfactory results. In view of this, we propose a personalized low-rank subspace clustering method, namely PLRLS, to learn more accurate subspace structures from both global and local perspectives. Specifically, we first introduce the local structure constraint to capture the local structure information of the data, while helping our method to obtain better inter-cluster separability and intra-cluster compactness. Then, in order to retain the important similarity information that is ignored by the LRR model, we utilize the fractional function to extract similarity information between cells, and introduce this information as the similarity constraint into the LRR framework. The fractional function is an efficient similarity measure designed for scRNA-seq data, which has theoretical and practical implications. In the end, based on the LRR matrix learned from PLRLS, we perform downstream analyses on real scRNA-seq datasets, including spectral clustering, visualization and marker gene identification. Comparative experiments show that the proposed method achieves superior clustering accuracy and robustness.

JBHI Journal 2023 Journal Article

Automatic Seizure Detection Using Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and Improved Deep Forest Learning

Shasha Yuan
Xiang Liu
Junliang Shang
Jin-Xing Liu
Juan Wang
Weidong Zhou

Automatic seizure detection could facilitate early detection, improve treatment planning, and reduce medical workload. This study describes a novel Logarithmic Euclidean-Gaussian Mixture Models (LE-GMMs) and an improved Deep Forest learning algorithm for epileptic seizure detection. The LE-GMMs could map the Riemannian manifold structure of Gaussian models to linear Euclidean space, which fully exploits the ability of GMMs to distinguish non-seizure and seizure EEG signals. The Multi-Pooling and error Screening Forest (MPSForest) learning method based on Deep Forest uses multi-pooling and out-of-bagging (OOB) error screening to reduce memory load and random tree construction. Firstly, variational modal decomposition (VMD) is applied to decompose electroencephalogram (EEG) signals into five layers, and the first three layers are chosen to construct EEG time-frequency distribution. Then Gaussian Mixture Models are estimated, and the LE-GMMs are constructed to extract valid EEG features. These features are input into the MPSForest model to classify seizure and non-seizure samples. After that, the outputs are subjected to post-processing to get the final seizure detection results, including moving average filtering and the adaptive collar technique. The proposed method achieves average sensitivity of 98. 22% and specificity of 98. 99% on the UPenn and Mayo Clinic dataset, and for the long-term Freiburg EEG dataset with 21 patients, the sensitivity of 98. 47% and specificity of 98. 57% are yielded respectively with the false detection rate of 0. 24/h. The experimental results show that this proposed method has excellent accuracy in distinguishing non-seizure and seizure EEG signals and holds great potential for clinical research and diagnostics.

JBHI Journal 2023 Journal Article

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data

Ying-Lian Gao
Qian Qiao
Juan Wang
Sha-Sha Yuan
Jin-Xing Liu

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

NeurIPS Conference 2023 Conference Paper

GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference

Ziang Li
Mengda Yang
Yaxin Liu
Juan Wang
Hongxin Hu
Wenzhe Yi
Xiaoyang Xu

Split Inference (SI) is an emerging deep learning paradigm that addresses computational constraints on edge devices and preserves data privacy through collaborative edge-cloud approaches. However, SI is vulnerable to Data Reconstruction Attacks (DRA), which aim to reconstruct users' private prediction instances. Existing attack methods suffer from various limitations. Optimization-based DRAs do not leverage public data effectively, while Learning-based DRAs depend heavily on auxiliary data quantity and distribution similarity. Consequently, these approaches yield unsatisfactory attack results and are sensitive to defense mechanisms. To overcome these challenges, we propose a GAN-based LAtent Space Search attack (GLASS) that harnesses abundant prior knowledge from public data using advanced StyleGAN technologies. Additionally, we introduce GLASS++ to enhance reconstruction stability. Our approach represents the first GAN-based DRA against SI, and extensive evaluation across different split points and adversary setups demonstrates its state-of-the-art performance. Moreover, we thoroughly examine seven defense mechanisms, highlighting our method's capability to reveal private information even in the presence of these defenses.

JBHI Journal 2023 Journal Article

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder

Ying Wang
Ying-Lian Gao
Juan Wang
Feng Li
Jin-Xing Liu

Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.

JBHI Journal 2023 Journal Article

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification

Juan Wang
Lin-Ping Wang
Sha-Sha Yuan
Feng Li
Jin-Xing Liu
Jun-Liang Shang

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering. In this study, we propose a novel matrix factorization method named NLRRC for single-cell type identification. NLRRC joins non-negative low-rank representation (LRR) and random walk graph regularized NMF (RWNMFC) to accurately reveal the natural grouping of cells. Specifically, we find the lowest rank representation of single-cell samples by non-negative LRR to reduce the difficulty of analyzing high-dimensional samples and capture the global information of the samples. Meanwhile, by using random walk graph regularization (RWGR) and NMF, RWNMFC captures manifold structure and cluster information before generating a cluster allocation matrix. The cluster assignment matrix contains cluster labels, which can be used directly to get the clustering results. The performance of NLRRC is validated on simulated and real single-cell datasets. The results of the experiments illustrate that NLRRC has a significant advantage in single-cell type identification.

JBHI Journal 2022 Journal Article

DeepRCI: Predicting ATP-Binding Proteins Using the Residue-Residue Contact Information

Zhaoxi Zhang
Yulan Zhao
Juan Wang
Maozu Guo

Adenine-5’-triphosphate (ATP) is a direct energy source for various activities of tissues and cells in the body. The release of ATP energies requires the assistance of ATP-binding proteins. Therefore, the identification of ATP-binding proteins is of great significance for the research on organisms. So far, there are several methods for predicting ATP-binding proteins. However, the accuracies of these methods are so low that the predicted proteins are inaccurate. Here, we designed a novel method, called as DeepRCI (based on Deep convolutional neural network and Residue-residue Contact Information), for predicting ATP-binding proteins. In order to maximize the performance of our method, we experimented with different hyperparameters and finally chose a 12-depth-512-filters deep convolutional neural network with an input size of 448*448. By using this model, DeepRCI achieved an accuracy of 93. 61% on the test set which means a significant improvement of 11. 78% over the state-of-the-art methods. We also compared the performance of residue-residue contact information datasets with different noise levels which are mainly due to gaps in the multiple sequence alignment. Compared with the low-noise dataset, the prediction accuracy on the high-noise dataset is reduced by 6. 78%, which affects the performance of DeepRCI to a certain extent. We believe that with the increase of sequence data, this problem will eventually be solved. Finally, we provide a web service of DeepRCI which link can be obtained in Data Availability.

NeurIPS Conference 2022 Conference Paper

Measuring Data Reconstruction Defenses in Collaborative Inference Systems

Mengda Yang
Ziang Li
Juan Wang
Hongxin Hu
Ao Ren
Xiaoyang Xu
Wenzhe Yi

The collaborative inference systems are designed to speed up the prediction processes in edge-cloud scenarios, where the local devices and the cloud system work together to run a complex deep-learning model. However, those edge-cloud collaborative inference systems are vulnerable to emerging reconstruction attacks, where malicious cloud service providers are able to recover the edge-side users’ private data. To defend against such attacks, several defense countermeasures have been recently introduced. Unfortunately, little is known about the robustness of those defense countermeasures. In this paper, we take the first step towards measuring the robustness of those state-of-the-art defenses with respect to reconstruction attacks. Specifically, we show that the latent privacy features are still retained in the obfuscated representations. Motivated by such an observation, we design a technology called Sensitive Feature Distillation (SFD) to restore sensitive information from the protected feature representations. Our experiments show that SFD can break through defense mechanisms in model partitioning scenarios, demonstrating the inadequacy of existing defense mechanisms as a privacy-preserving technique against reconstruction attacks. We hope our findings inspire further work in improving the robustness of defense mechanisms against reconstruction attacks for collaborative inference systems.

JBHI Journal 2022 Journal Article

Multi-View Random-Walk Graph Regularization Low-Rank Representation for Cancer Clustering and Differentially Expressed Gene Selection

Juan Wang
Li-Hong Wang
Jin-Xing Liu
Xiang-Zhen Kong
Sheng-Jun Li

Cancer genome data generally consists of multiple views from different sources. These views provide different levels of information about gene activity, as well as more comprehensive cancer information. The low-rank representation (LRR) method, as a powerful subspace clustering method, has been extended and applied in cancer data research. Although the multi-view learning methods based on low rank representation have achieved good results in cancer multi-omics analysis because they fully consider the consistency and complementarity between views, these methods have some shortcomings in mining the potential local geometry of data. In view of this, this paper proposes a new method named Multi-view Random-walk Graph regularization Low-Rank Representation (MRGLRR) to comprehensively analyze multi-view genomics data. This method uses multi-view model to find the common centroid of view. By constructing a joint affinity matrix to learn the low-rank subspace representation of multiple sets of data, the hidden information of each view is fully obtained. In addition, this method introduces random walk graph regularization constraint to obtain more accurate similarity between samples. Different from the traditional graph regularization constraint, after constructing the KNN graph, we use the random walk algorithm to obtain the weight matrix. The random walk algorithm can retain more local geometric information and better learn the topological structure of the data. What's more, a feature gene selection strategy suitable for multi-view model is proposed to find more differentially expressed genes with research value. Experimental results show that our method is better than other representative methods in terms of clustering and feature gene selection for cancer multi-omics data.

TCS Journal 2022 Journal Article

On the complexity of local-equitable coloring of graphs

Zuosong Liang
Juan Wang
Junqing Cai
Xinxin Yang

An equitable k-partition ( k ≥ 2 ) of a vertex set S is a partition of S into k disjoint subsets (may be empty sets) such that the sizes of any two subsets of S differ by at most one. A local-equitable k-coloring of G is an assignment of k colors to the vertices of G such that, for every maximal clique of G, the coloring of this clique forms an equitable k-partition of itself. The local-equitable coloring of G is a stronger version of clique-coloring of graphs. Chordal graphs are 2-clique-colorable but not necessarily local-equitably 2-colorable. In this paper, we prove that it is NP-complete to decide the local-equitable 2-colorability in chordal graphs and even in split graphs. In addition, we prove that claw-free split graphs are local-equitably k-colorable when k ≤ 4, but not necessarily local-equitably k-colorable when k ≥ 5. A sufficient and sharp condition of local-equitably k-colorability is also given in claw-free split graphs. Secondly, we show that, given a split graph G, deciding the local-equitable k-colorability of G is solvable in polynomial time when k = ω ( G ) − 1, where ω ( G ) is the clique number of G. At last, we prove that the decision problem of local-equitable 2-coloring of planar graphs is solvable in polynomial time.

JBHI Journal 2022 Journal Article

SLRRSC: Single-Cell Type Recognition Method Based on Similarity and Graph Regularization Constraints

Na-Na Zhang
Jin-Xing Liu
Chun-Hou Zheng
Juan Wang

Single-cell clustering is a crucial task of scRNA-seq analysis, which reveals the natural grouping of cells. However, due to the high noise and high dimension in scRNA-seq data, how to effectively and accurately identify cell types from a great quantity of cell mixtures is still a challenge. Considering this, in this paper, we propose a novel subspace clustering algorithm termed SLRRSC. This method is developed based on the low-rank representation model, and it aims to capture the global and local properties inherent in data. In order to make the LRR matrix describe the spatial relationship of samples more accurately, we introduce the manifold-based graph regularization and similarity constraint into the LRR-based method SLRRSC. The graph regularization can preserve the local geometric structure of the data in low-rank decomposition, so that the low-rank representation matrix contains more local structure information. By imposing similarity constraint on the low-rank matrix, the similarity information between sample pairs is further introduced into the SLRRSC model to improve the learning ability of low-rank method for global structure. At the same time, the similarity constraint makes the low-rank representation matrix symmetric, which makes it better interpretable in clustering application. We compare the effectiveness of the SLRRSC algorithm with other single-cell clustering methods on simulated data and real single-cell datasets. The results show that this method can obtain more accurate sample similarity matrix and effectively solve the problem of cell type recognition.

JBHI Journal 2022 Journal Article

Visualization and Analysis of Single Cell RNA-Seq Data by Maximizing Correntropy Based Non-Negative Low Rank Representation

Cui-Na Jiao
Jin-Xing Liu
Juan Wang
Junliang Shang
Chun-Hou Zheng

The exploration of single cell RNA-sequencing (scRNA-seq) technology generates a new perspective to analyze biological problems. One of the major applications of scRNA-seq data is to discover subtypes of cells by cell clustering. Nevertheless, it is challengeable for traditional methods to handle scRNA-seq data with high level of technical noise and notorious dropouts. To better analyze single cell data, a novel scRNA-seq data analysis model called Maximum correntropy criterion based Non-negative and Low Rank Representation (MccNLRR) is introduced. Specifically, the maximum correntropy criterion, as an effective loss function, is more robust to the high noise and large outliers existed in the data. Moreover, the low rank representation is proven to be a powerful tool for capturing the global and local structures of data. Therefore, some important information, such as the similarity of cells in the subspace, is also extracted by it. Then, an iterative algorithm on the basis of the half-quadratic optimization and alternating direction method is developed to settle the complex optimization problem. Before the experiment, we also analyze the convergence and robustness of MccNLRR. At last, the results of cell clustering, visualization analysis, and gene markers selection on scRNA-seq data reveal that MccNLRR method can distinguish cell subtypes accurately and robustly.

JBHI Journal 2021 Journal Article

Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations

Ming-Ming Gao
Zhen Cui
Ying-Lian Gao
Juan Wang
Jin-Xing Liu

As we all know, science and technology are developing faster and faster. Many experts and scholars have demonstrated that human diseases are related to lncRNA, but only a few associations have been confirmed, and many unknown associations need to be found. In the process of finding associations, it takes a lot of time, so finding an efficient way to predict the associations between lncRNAs and diseases is particularly important. In this paper, we propose a multi-label fusion collaborative matrix factorization (MLFCMF) approach for predicting lncRNA-disease associations (LDAs). Firstly, the lncRNA space and disease space are optimized by multi-label to enhance the intrinsic link between lncRNA and disease and to tap potential information. Multi-label learning can encode a variety of data information from the sample space. Secondly, to learn multi-label information in the data space, the fusion method is used to handle the relationship between multiple labels. More comprehensive information will be obtained by weighing the effects of different labels. The addition of Gaussian interaction profile (GIP) kernel can increase the network similarity. Finally, the lncRNA-disease associations are predicted by the method of collaborative matrix factorization. The ten-fold cross-validation method is used to evaluate the MLFCMF method, and our method finally obtains an AUC value of 0. 8612. Detailed analysis of ovarian cancer, colorectal cancer, and lung cancer in the simulation experiment results. So it can be seen that our method MLFCMF is an effective model for predicting lncRNA-disease associations.

JBHI Journal 2020 Journal Article

Integrative Hypergraph Regularization Principal Component Analysis for Sample Clustering and Co-Expression Genes Network Analysis on Multi-Omics Data

Ming-Juan Wu
Ying-Lian Gao
Jin-Xing Liu
Chun-Hou Zheng
Juan Wang

In recent years, with the diversity and variability of cancer information, the multi-omics data have been applied in various fields. Many existing models of principal component analysis can only process single data, which makes limitations on cancer research. Therefore, in this paper, a new model called integrative principal component analysis (IPCA) is proposed to achieve the unification of multi-omics data. In addition, in order to preserve the high-order manifold structure between the data, an integrative hypergraph regularization principal component analysis (IHPCA) is further proposed by applying the hypergraph regularization constraint. The effectiveness of IHPCA method is tested on four multi-omics datasets. Experimental results show that the proposed method has better performance than other representative methods on sample clustering and common expression genes (co-expression genes) network analysis.

JBHI Journal 2020 Journal Article

Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning

Juan Wang
Yujing Bai
Bin Xia

Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus limiting their application in clinic. Simultaneous prediction of DR related features during the DR severity diagnosis is able to resolve this issue by providing supporting evidence (i. e. DR related features) for the diagnostic result (i. e. DR severity). In this study, we propose a hierarchical multi-task deep learning framework for simultaneous diagnosis of DR severity and DR related features in fundus images. A hierarchical structure is introduced to incorporate the casual relationship between DR related features and DR severity levels. In the experiments, the proposed approach was evaluated on two independent testing sets using quadratic weighted Cohen's kappa coefficient, receiver operating characteristic analysis, and precision-recall analysis. A grader study was also conducted to compare the performance of the proposed approach with those of general ophthalmologists with different levels of experience. The results demonstrate that the proposed approach could improve the performance for both DR severity diagnosis and DR related feature detection when comparing with the traditional deep learning-based methods. It achieves performance close to general ophthalmologists with five years of experience when diagnosing DR severity levels, and general ophthalmologists with ten years of experience for referable DR detection.

YNIMG Journal 2014 Journal Article

Theta–gamma coupling reflects the interaction of bottom-up and top-down processes in speech perception in children

Juan Wang
Danqi Gao
Duan Li
Amy S. Desroches
Li Liu
Xiaoli Li

This study investigates how the interaction of different brain oscillations (particularly theta–gamma coupling) modulates the bottom-up and top-down processes during speech perception. We employed a speech perception paradigm that manipulated the congruency between a visually presented picture and an auditory stimulus and asked participants to judge whether they matched or mismatched. A group of children (mean age 10years, 5months) participated in this study and their electroencephalographic (EEG) data were recorded while performing the experimental task. It was found that in comparison with mismatch condition, match condition facilitated speech perception by eliciting greater theta–gamma coupling in the frontal area and smaller theta–gamma coupling in the left temporal area. These findings suggested that a top-down facilitation effect from congruent visual pictures engaged different mechanisms in low-level sensory (temporal) regions and high-level linguistic and decision (frontal) regions. Interestingly, hemispheric asymmetry is with higher theta–gamma coupling in the match condition in the right hemisphere and higher theta–gamma coupling in the mismatch condition in the left hemisphere. This indicates that a fast global processing strategy and a slow detailed processing strategy were differentially adopted in the match and mismatch conditions. This study provides new insight into the mechanisms of speech perception from the interaction of different oscillatory activities and provides neural evidence for theories of speech perception allowing for top-down feedback connections. Furthermore, it sheds light on children's speech perception development by showing a similar pattern of integration of bottom-up and top-down information during speech perception as previous studies have revealed in adults.