Search

Your search keyword '"Hu, Dayu"' showing total 33 results
Start Over Author "Hu, Dayu"
33 results on '"Hu, Dayu"'

1. Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types

Author

Hu, Dayu, Liang, Ke, Yu, Hao, and Liu, Xinwang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Molecular Networks
Abstract

In recent years, the field of single-cell data analysis has seen a marked advancement in the development of clustering methods. Despite advancements, most of these algorithms still concentrate on analyzing the provided single-cell matrix data. However, in medical applications, single-cell data often involves a wealth of exogenous information, including gene networks. Overlooking this aspect could lead to information loss and clustering results devoid of significant clinical relevance. An innovative single-cell deep clustering method, incorporating exogenous gene information, has been proposed to overcome this limitation. This model leverages exogenous gene network information to facilitate the clustering process, generating discriminative representations. Specifically, we have developed an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. Concurrently, we conducted a random walk on an exogenous Protein-Protein Interaction (PPI) network, thereby acquiring the gene's topological features. Ultimately, during the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. Extensive experiments have validated the effectiveness of our proposed method. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.

Published
2023

2. Dual-channel Prototype Network for few-shot Classification of Pathological Images

Author

Quan, Hao, Li, Xinjia, Hu, Dayu, Nan, Tianhang, and Cui, Xiaoyu

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In pathology, the rarity of certain diseases and the complexity in annotating pathological images significantly hinder the creation of extensive, high-quality datasets. This limitation impedes the progress of deep learning-assisted diagnostic systems in pathology. Consequently, it becomes imperative to devise a technology that can discern new disease categories from a minimal number of annotated examples. Such a technology would substantially advance deep learning models for rare diseases. Addressing this need, we introduce the Dual-channel Prototype Network (DCPN), rooted in the few-shot learning paradigm, to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer (PVT) framework for few-shot classification via self-supervised learning and integrates it with convolutional neural networks. This combination forms a dual-channel architecture that extracts multi-scale, highly precise pathological features. The approach enhances the versatility of prototype representations and elevates the efficacy of prototype networks in few-shot pathological image classification tasks. We evaluated DCPN using three publicly available pathological datasets, configuring small-sample classification tasks that mirror varying degrees of clinical scenario domain shifts. Our experimental findings robustly affirm DCPN's superiority in few-shot pathological image classification, particularly in tasks within the same domain, where it achieves the benchmarks of supervised learning.

Published
2023

3. TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

Author

Liu, Meng, Liang, Ke, Hu, Dayu, Yu, Hao, Liu, Yue, Meng, Lingyuan, Tu, Wenxuan, Zhou, Sihang, and Liu, Xinwang

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra- and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate TMac outperforms other SOTA models in performance. Our code is available at https://github.com/MGitHubL/TMac., Comment: This work has been accepted by ACM MM 2023 for publication

Published
2023
Full Text
View/download PDF

15. High-order Topology for Deep Single-Cell Multiview Fuzzy Clustering

Author

Hu, Dayu, Dong, Zhibin, Liang, Ke, Yu, Hao, Wang, Siwei, and Liu, Xinwang

Abstract

Single-cell multiview clustering is essential for analyzing the different cell subtypes of the same cell from different views. Some attempts have been made, but most of these models still struggle to handle single-cell sequencing data, primarily due to their nonspecific design for cellular data. We observe that such data distinctively exhibits: 1) a profusion of high-order topological correlations, 2) a disparate distribution of information across different views, and 3) inherent fuzzy characteristics, indicating a cell's potential to associate with multiple cluster identities. Neglecting these key cellular patterns could significantly impair medical clustering. In response, we propose a specialized application of fuzzy clustering for single-cell sequencing data, namely, the deep single-cell multiview fuzzy clustering method. Concretely, we employ a random walk technique to capture high-order topological relationships on the cell graph and have developed a cross-view information aggregation mechanism that adaptively assigns weights to different views. Furthermore, to accurately reflect the dynamic insight in cellular development, we propose a deep fuzzy clustering strategy that allows cells to associate with diverse clusters. Extensive experiments conducted on three real-world single-cell multiview datasets demonstrate our method's superior performance.

Published
2024
Full Text
View/download PDF

18. Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters

Author

Hu, Dayu, Dong, Zhibin, Liang, Ke, Wang, Jun, Wang, Siwei, Liu, Xinwang, Hu, Dayu, Dong, Zhibin, Liang, Ke, Wang, Jun, Wang, Siwei, and Liu, Xinwang

Abstract

Single-cell multi-view clustering enables the exploration of cellular heterogeneity within the same cell from different views. Despite the development of several multi-view clustering methods, two primary challenges persist. Firstly, most existing methods treat the information from both single-cell RNA (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) views as equally significant, overlooking the substantial disparity in data richness between the two views. This oversight frequently leads to a degradation in overall performance. Additionally, the majority of clustering methods necessitate manual specification of the number of clusters by users. However, for biologists dealing with cell data, precisely determining the number of distinct cell types poses a formidable challenge. To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters. The scUNC method comprises several steps: initially, it employs a cross-view fusion network to create an effective embedding, which is then utilized to generate initial clusters via community detection. Subsequently, the clusters are automatically merged and optimized until no further clusters can be merged. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets. The results underscored that scUNC outperforms the other baseline methods.

Published
2023

19. Spatial–Spectral Graph Contrastive Clustering With Hard Sample Mining for Hyperspectral Images

Author

Guan, Renxiang, Tu, Wenxuan, Li, Zihao, Yu, Hao, Hu, Dayu, Chen, Yuzeng, Tang, Chang, Yuan, Qiangqiang, and Liu, Xinwang

Abstract

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups image pixels with similar features into distinct clusters. Among various approaches, contrastive learning methods, which employ the concept of encouraging semantically similar samples to move closer together while pushing semantically inconsistent samples apart, have garnered significant attention due to their promising performance. However, the most prevalent approaches face two major limitations: 1) treating all samples indiscriminately during optimization, where the abundance of well-categorized samples overwhelms the feature learning process and 2) tending to introduce noise when constructing positive sample pairs through view augmentation or searching the nearest neighbors, which would cause semantic drift of sample features. To solve these issues, we propose a graph autoencoder-based deep clustering framework named spatial–spectral graph contrastive clustering with hard sample mining (SSGCC) that constructs spatial–spectral dual views without data augmentation and focuses more on hard samples rather than treating all samples equally with the aid of spatial–spectral features. Concretely, we extract the spectral features and the neighborhood spatial features of the samples as dual branches to avoid the noise caused by data augmentation and develop the cluster-oriented consistency learning to facilitate the exchange of knowledge between the two spectral–spatial perspectives. In addition, we propose a hard sample mining-based contrastive learning scheme with the aid of spatial–spectral features. To better measure the importance of the samples, we combine spatial features and spectral features to calculate the similarity between sample pairs. The weights of hard sample pairs are dynamically up-weight while the easy ones are down-weighting to improve the discriminative capability. Extensive experiments on four benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed methods against state-of-the-art ones.

Published
2024
Full Text
View/download PDF

20. Dual-Channel Prototype Network for Few-Shot Pathology Image Classification

Author

Quan, Hao, Li, Xinjia, Hu, Dayu, Nan, Tianhang, and Cui, Xiaoyu

Abstract

In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning has demonstrated unique advantages in modeling tasks with limited data, yet explorations of this method in the field of pathology remain in the early stages. To address this issue, we present a dual-channel prototype network (DCPN), a novel few-shot learning approach for efficiently classifying pathology images with limited data. The DCPN leverages self-supervised learning to extend the pyramid vision transformer (PVT) to few-shot classification tasks and combines it with a convolutional neural network to construct a dual-channel network for extracting multi-scale, high-precision pathological features, thereby substantially enhancing the generalizability of prototype representations. Additionally, we design a soft voting classifier based on multi-scale features to further augment the discriminative power of the model in complex pathology image classification tasks. We constructed three few-shot classification tasks with varying degrees of domain shift using three publicly available pathological datasets—CRCTP, NCTCRC, and LC25000—to emulate real-world clinical scenarios. The results demonstrated that the DCPN outperformed the prototypical network across all metrics, achieving the highest accuracies in same-domain tasks—70.86% for 1-shot, 82.57% for 5-shot, and 85.2% for 10-shot setups—corresponding to improvements of 5.51%, 5.72%, and 6.81%, respectively, over the prototypical network. Notably, in the same-domain 10-shot setting, the accuracy of the DCPN (85.2%) surpassed that of the PVT-based supervised learning model (85.15%), confirming its potential to diagnose rare diseases within few-shot learning frameworks.

Published
2024
Full Text
View/download PDF

21. Joint effect of multiple air pollutants on cardiometabolic health in normal-weight and obese adults: A novel insight into the role of circulating free fatty acids

Author

Zhang, Wenlou, primary, Wang, Wanzhou, additional, Li, Luyi, additional, Miller, Mark R., additional, Cui, Liyan, additional, Liu, Junxiu, additional, Wang, Yang, additional, Hu, Dayu, additional, Liu, Shan, additional, Xu, Junhui, additional, Wu, Shaowei, additional, Duan, Junchao, additional, Sun, Zhiwei, additional, Guo, Xinbiao, additional, and Deng, Furong, additional

Published
2023
Full Text
View/download PDF

31. scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.

Author

Hu D, Guan R, Liang K, Yu H, Quan H, Zhao Y, Liu X, and He K

Subjects
Cluster Analysis, Humans, Transcriptome, Computational Biology methods, Gene Expression Profiling methods, Protein Interaction Maps genetics, Gene Regulatory Networks, Single-Cell Analysis methods, Algorithms
Abstract

In recent years, there has been significant advancement in the field of single-cell data analysis, particularly in the development of clustering methods. Despite these advancements, most algorithms continue to focus primarily on analyzing the provided single-cell matrix data. However, within medical contexts, single-cell data often encompasses a wealth of exogenous information, such as gene networks. Overlooking this aspect could result in information loss and produce clustering outcomes lacking significant clinical relevance. To address this limitation, we introduce an innovative deep clustering method for single-cell data that leverages exogenous gene information to generate discriminative cell representations. Specifically, an attention-enhanced graph autoencoder has been developed to efficiently capture topological signal patterns among cells. Concurrently, a random walk on an exogenous protein-protein interaction network enabled the acquisition of the gene's embeddings. Ultimately, the clustering process entailed integrating and reconstructing gene-cell cooperative embeddings, which yielded a discriminative representation. Extensive experiments have demonstrated the effectiveness of the proposed method. This research provides enhanced insights into the characteristics of cells, thus laying the foundation for the early diagnosis and treatment of diseases. The datasets and code can be publicly accessed in the repository at https://github.com/DayuHuu/scEGG., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
Full Text
View/download PDF

32. The relationship between personal exposure and ambient PM 2.5 and black carbon in Beijing.

Author

Lin C, Hu D, Jia X, Chen J, Deng F, Guo X, Heal MR, Cowie H, Wilkinson P, Miller MR, and Loh M

Subjects
Beijing, Carbon, China, Environmental Exposure analysis, Environmental Monitoring, Humans, Particulate Matter analysis, Air Pollutants analysis, Air Pollution analysis
Abstract

This study is part of the "Air Polluion Impacts on Cardiopulmonary disease in Beijing: an integrated study of Exposure Science, Toxicologenomics & Environmental Epidemiology (APIC-ESTEE)" project under the UK-China joint research programme "Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China)". The aim is to capture the spatio-temporal variability in people's exposure to fine particles (PM 2.5 ) and black carbon (BC) air pollution in Beijing, China. A total of 120 students were recruited for a panel study from ten universities in Haidian District in northwestern Beijing from December 2017 to June 2018. Real-time personal concentrations of PM 2.5 and BC were measured over a 24-h period with two research-grade portable personal exposure monitors. Personal microenvironments (MEs) were determined by applying an algorithm to the handheld GPS unit data. On average, the participants spent the most time indoors (79% in Residence and 16% in Workplace), and much less time travelling by Walking, Cycling, Bus and Metro. Similar patterns were observed across participant gender and body-mass index classifications. The participants were exposed to 33.8 ± 27.8 μg m -3  PM 2.5 and to 1.9 ± 1.2 μg m -3  BC over the 24-h monitoring period, on average 24.3 μg m -3 (42%) and 0.8 μg m -3 (28%) lower, respectively, than the concurrent fixed-site ambient measurements. Relative differences between personal and ambient BC concentrations showed greater variability across the MEs, highlighting significant contributions from Dining and travelling by Bus, which involve potential combustion of fuels. This study demonstrates the potential value of personal exposure monitoring in investigating air pollution related health effects, and in evaluating the effectiveness of pollution control and intervention measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2020
Full Text
View/download PDF

33. The modification of indoor PM 2.5 exposure to chronic obstructive pulmonary disease in Chinese elderly people: A meet-in-metabolite analysis.

Author

Huang Q, Hu D, Wang X, Chen Y, Wu Y, Pan L, Li H, Zhang J, Deng F, Guo X, and Shen H

Subjects
Aged, Aged, 80 and over, Air Pollutants urine, China epidemiology, Chromatography, Liquid, Cross-Sectional Studies, Female, Humans, Male, Mass Spectrometry, Metabolomics, Middle Aged, Particulate Matter urine, Prevalence, Pulmonary Disease, Chronic Obstructive chemically induced, Air Pollutants adverse effects, Air Pollution, Indoor adverse effects, Environmental Exposure, Particulate Matter adverse effects, Pulmonary Disease, Chronic Obstructive epidemiology
Abstract

Background: Exposure to airborne fine particulate matter (PM 2.5 ) has been associated with a variety of adverse health outcomes including chronic obstructive pulmonary disease (COPD). However, the linkages between PM 2.5 exposure, PM 2.5 -related biomarkers, COPD-related biomarkers and COPD remain poorly elucidated., Objectives: To investigate the linkages between PM 2.5 exposure and COPD outcome by using the meet-in-middle strategy based on urinary metabolic biomarkers., Methods: A cross-sectional study was designed to illustrate the mentioned quadripartite linkages. Indoor PM 2.5 and its element components were assessed in 41 Chinese elderly participants including COPD patients and their healthy spouses. Metabolic biomarkers involved in PM 2.5 exposure and COPD were identified by using urinary metabolomics. The associations between PM 2.5 - and COPD-related biomarkers were investigated by statistics and metabolic pathway analysis., Results: Seven metabolites were screened and identified with significant correlations to PM 2.5 exposure, which were majorly involved in purine and amino acid metabolism as well as glycolysis. Ten COPD-related metabolic biomarkers were identified, which suggested that amino acid metabolism, lipid and fatty acid metabolism, and glucose metabolism were disturbed in the patients. Also, PM 2.5 and its many elemental components were significantly associated with COPD-related biomarkers. We observed that the two kinds of biomarkers (PM 2.5 - and COPD-related) integrated in a locally connected network and the alterations of these metabolic biomarkers can biologically link PM 2.5 exposure to COPD outcome., Conclusions: Our study indicated the modification of PM 2.5 to COPD via both modes of action of lowering participants' antioxidation capacity and decreasing their lung energy generation; this information would be valuable for the prevention strategy of COPD., (Copyright © 2018. Published by Elsevier Ltd.)

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results