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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lei Xu Jianhong Cheng Jin Liu Hulin Kuang Fan Wu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (纸本)9781665429825

Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifically, we extract two types of features from adventitious respiratory sound, including Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram. The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task. Moreover, the channel-spatial attention can enhance the feature representation, in which the channel attention explores the inter-channel relationship of the spectrums, and then the inter-spatial correlation mapping is generated by the spatial attention introduced serially. We evaluate our proposed method on ICBHI 2017 database. Experimental results show that our proposed method achieves encouraging predictive performance with an accuracy of 80.0% for identifying abnormal sounds from normal sounds, and with an accuracy of 92.4% for distinguishing crackles from wheezes. In addition, our method also achieves a score of 56.76% for the four-class sound classification of adventitious sounds and outperforms several state-of-the-art methods.

关键词： Correlation Databases Fuses Cepstral analysis Conferences Neural networks Lung

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Hybrid region and population hypergraph neural network for mild cognitive impairment detection

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Pattern Recognition 2026年 169卷

作者： Jie Wang Luohuang Wu Hulin Kuang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

Mild Cognitive Impairment (MCI) detection is paramount in Alzheimer’s disease (AD) prevention. Existing methods have not sufficiently leveraged both subject correlation and brain region topological information into MCI detection. To solve the limitation, this study proposes a hybrid region and population hypergraph neural network for MCI detection. Specifically, we use two-view selection on both signals and brain regions, and an improved metric learning-based module to improve the quality of hypergraphs. Additionally, a population-to-region hypergraph neural network is designed to leverage information from brain region topology and subject correlation for MCI detection. Experimental results show that our proposed method achieves accuracies of 87.98%, 81.05%, 86.42% and 82.95% for MCI detection, early MCI detection, late MCI detection and MCI classification on the AD Neuroimaging Initiative dataset, respectively, outperforming several state-of-the-art methods. Furthermore, experimental results on the Autism Brain Imaging Data Exchange dataset validating its generalization to other brain disorder detection tasks.

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Linear time algorithms for k-means with multi-swap local search 23

Linear time algorithms for k-means with multi-swap local sea...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Junyu Huang Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Department of Computer Science and Engineering State University of New York at Buffalo NY School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China and The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the singleswap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size. However, compared with multi-swap local search algorithms, there is a considerable gap on the approximation ratios of the single-swap local search algorithms. Although the current multi-swap local search algorithms provide small constant approximation, the proposed algorithms tend to have large polynomial running time, which cannot be used to handle large-scale datasets. In this paper, we propose a multi-swap local search algorithm for the k-means problem with linear running time in the data size. Given a swap size t, our proposed algorithm can achieve a (50(1 + 1/t) + ε)-approximation, which improves the current best result 509 (ICML 2019) with linear running time in the data size. Our proposed method, compared with previous multi-swap local search algorithms, is the first one to achieve linear running time in the data size. To obtain a more practical algorithm for the problem with better clustering quality and running time, we propose a sampling-based method which accelerates the process of clustering cost update during swaps. Besides, a recombination mechanism is proposed to find potentially better solutions. Empirical experiments show that our proposed algorithms achieve better performances compared with branch and bound solver (NeurIPS 2022) and other existing state-of-the-art local search algorithms on both small and large datasets.

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Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication

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Quantitative Biology 2021年第2期9卷 201-215页

作者： Kang Kang Xue Sun Lizhong Wang Xiaotian Yao Senwei Tang Junjie Deng Xiaoli Wu WeGene Research Team Can Yang Gang Chen Shenzhen WeGene Clinical Laboratory Shenzhen 518118China WeGene Shenzhen Zaozhidao Technology Co.Ltd.Shenzhen 518042China Department of Mathematics The Hong Kong University of Science and TechnologyHong KongChina Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

Background:The direct-to-consumer genetic testing(DTC-GT)industry has exploded in recent years,initiated by market pioneers from the United States and quickly followed by companies from Europe and *** addition to their primary objective of providing ancestry and health information to customers,DTC-GT services have emerged as a valuable data resource for large-scale population and genetics ***:We assessed DTC-GT market leaders in the *** China,user participation in research,and academic reports based on this *** also investigated DTC-GT end-user value by tracing key updates of companies provided via health risk reports and evaluating their predictive *** then assessed the replicability of several genome-wide association studies(GWAS)based on a Chinese DTC-GT ***:As recent entrants to the market,Chinese DTC-GT serv ice providers have published less academic research than their Western counterparts;however,a larger proportion of Chinese users consent to participate in research *** increases in user volume and resultant report updates led to reclassification of some users'polygenic risk levels,but within a reasonable scale and with increased predictive *** among GWAS using the Chinese DTC-GT biobank varied by studied trait,population background,and sample ***:We speculate that the rapid growth in DTC-GT services,particularly in non-Caucasian populations,will yield an important and much-needed resource for biobanking,large-scale genetic studies,clinical trials,and post-clinical applications.

关键词： DTC-GT biobank Chinese population polygenic risk GWAS replication

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BPMN++: Comprehensive Business Process Modeling for Industrial Internet Application

BPMN++: Comprehensive Business Process Modeling for Industri...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hangyu Cheng Guosheng Kang Jianxun Liu Yiping Wen Buqing Cao Zhen Wang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process models. However, BPMN cannot support the intuitive modeling for some typical tasks in complex business scenarios of the Industrial Internet context, increasing the communication cost between modelers and business personnel. The current solutions try to extend the attributes of BPMN elements with annotations, but a large number of annotations makes business process models complex and cumbersome. This paper proposes to extend new business process modeling elements for Industrial Internet application scenarios based on BPMN extension mechanism, generating an extended business process modeling language, namely BPMN++. And an extended process modeling tool is implemented, which is available online. A case study over a real-world Industrial Internet process validates the usefulness of this extension mechanism and the well understandability of business process models.

关键词： Industries Process modeling Annotations Internet Personnel Task analysis Optimization

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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GraphIsoFun: a graph neural network based approach for splice isoform function prediction

GraphIsoFun: a graph neural network based approach for splic...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Shuo Zhang Changhuo Yang Hong-Dong Li Jianxin Wang School of Computer Science and Engineering Central South University Changsha China Hunan Hunan Provincial Key Lab on Bioinformatics Central South University Changsha China Hunan

ISBN: (纸本)9781665429825

Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.

关键词： Proteins Protein engineering Costs Annotations Databases Splicing Conferences

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Improving human essential protein prediction using only protein sequences via ensemble learning

Improving human essential protein prediction using only prot...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Min Zeng Nian Wang Yifan Wu Yiming Li Fang-Xiang Wu Min Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9781665429825

Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://***/CSUBioGroup/EP-GBDT.

关键词： Proteins Protein engineering Costs Codes Conferences Predictive models Biomedical measurement

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Improving the Prediction of Disease-associated Genes by Integrating Annotated Gene Sets

Improving the Prediction of Disease-associated Genes by Inte...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Chao Deng Cui-Xiang Lin Hong-Dong Li Human Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Identifying disease-associated genes is key to studying the pathogenic mechanism of complex diseases. Existing methods for predicting disease genes are dominantly based on molecular networks or omics data, including gene expression, protein expression, co-expression networks, protein-protein interaction, etc. The annotated gene sets such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways represent knowledge about the functional association between genes, which may be of predictive value for disease gene identification. However, the knowledge is barely explored in existing approaches. In this study, we propose a new method for predicting disease-associated genes by integrating annotated gene sets. It first constructs a signal matrix by integrating annotated gene sets in the MSigDB data, a comprehensive database of curated gene sets. Then it uses the signal matrix as input features to build a prediction model with machine learning approaches. We compared our method with existing disease gene prediction methods on five complex diseases. The results showed that our method is superior to other methods.

关键词： Proteins Genomics Machine learning Predictive models Ontologies Prediction algorithms Graph neural networks

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