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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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SCOTCluster: Deep Clustering with Optimal Transport for Large-scale Single-cell RNA-seq Data

SCOTCluster: Deep Clustering with Optimal Transport for Larg...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Long, Faning DIng, Xiaojun Peng, Xiaoqing Wang, Jianxin Zhu, Xiaoshu Yulin Normal University School of Computer Science and Engineering Yulin China Central South University Center for Medical Genetics School of Life Sciences Changsha China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9781665401265

Single-cell RNA sequencing (scRNA-seq) presents cell heterogeneity in a high resolution to explore cell development. The high dimension and the high noise in scRNAseq data bring some computational challenges. By introducing optimal transmission regularization, we proposed a novel deep clustering method, called SCOTCluster, which accurately learned low-dimensional representations. In SCOTCluster, a joint training strategy was designed by integrating AutoEncoder and soft k-means. Notably, to improve simultaneously the accuracy and robustness, the optimal transmission was introduced in the objective function of soft k-means, and entropy regularization and Sinkhorn iterative algorithm were performed to constraint the cluster size. To test the performance, we compared SCOTCluster with five state-of the-art methods on 16 real large-scale scRNA-seq datasets. The experimental results showed that SCOTCluster improved the training stability and clustering performance. © 2021 IEEE.

关键词： RNA

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De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization 1

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Wu, Gaoyan Yang, Mengyun Li, Yaohang Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan Shaoyang University Shaoyang422000 China Department of Computer Science Old Dominion University NorfolkVA23529 United States

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

The identification of drug-target interactions plays a crucial role in drug discovery and design. However, capturing interactions between drugs and targets via traditional biochemical experiments is an extremely laborious, expensive and time-consuming procedure. Therefore, the use of computational methods for predicting potential interactions to guide the experimental verification has attracted a lot of attention. In this paper, we propose a new algorithm, named Laplacian Regularized Schatten-p Norm Minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets. First, we take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten-p norm minimization model to improve prediction performance in the new drug/target cases by combining the loss function with a Laplacian regularization term. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers (ADMM) algorithm. Performance evaluations on benchmark datasets show that LRSpNM achieves better and more robust performance than five state-of-the-art drug-target interaction prediction algorithms. In addition, we conduct case study in practical applications, which also illustrates the effectiveness of our proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Deep learning Radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma

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Radiotherapy and Oncology 2025年 209卷 110949页

作者： Xiaochun Yi Xiaoping Yu Congrui Li Junjian Li Hui Cao Qiang Lu Junjun Li Jing Hou Department of Diagnostic Radiology Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China Hunan Provincial Key Laboratory on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 PR China Department of Health Service Center Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China Department of Pathology Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China

Purpose To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients. Materials and methods A cohort of 343 LANPC patients with pretreatment MRI and whole slide image (WSI) were randomly divided into training (n = 202), validation (n = 91), and external test (n = 50) sets. For WSIs, a self-attention mechanism was employed to assess the significance of different patches for the prognostic task, aggregating them into a WSI-level representation. For MRI, a multilayer perceptron was used to encode the extracted radiomic features, resulting in an MRI-level representation. These were combined in a multimodal fusion model to produce prognostic predictions. Model performances were evaluated using the concordance index (C-index), and Kaplan-Meier curves were employed for risk stratification. To enhance model interpretability, attention-based and Integrated Gradients techniques were applied to explain how WSIs and MRI features contribute to prognosis predictions. Results The radiopathomics model achieved high predictive accuracy in predicting the OS, with a C-index of 0.755 (95 % CI: 0.673–0.838) and 0.744 (95 % CI: 0.623–0.808) in the training and validation sets, respectively, outperforming single-modality models (radiomic signature: 0.636, 95 % CI: 0.584–0.688; deep pathomic signature: 0.736, 95 % CI: 0.684–0.810). In the external test, similar findings were observed for the predictive performance of the radiopathomics, radiomic signature, and deep pathomic signature, with their C-indices being 0.735, 0.626, and 0.660 respectively. The radiopathomics model effectively stratified patients into high- and low-risk groups ( P < 0.001). Additionally, attention heatmaps revealed that high-attention regions corresponded with tumor areas in both risk groups. Conclusion The radiopathomics model holds promise for predicting clinical outcomes in LANPC patients, offering a potential

关键词： Deep learning Digital pathology Magnetic resonance imaging Nasopharyngeal carcinoma Prognosis Risk stratification

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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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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Zeng, Min Wang, Nian Wu, Yifan Li, Yiming Wu, Fang-Xiang Li, Min Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering SaskatoonSKS7N 5A9 Canada

ISBN: (纸本)9781665401265

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. © 2021 IEEE.

关键词： Decision trees

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BPMN++: Comprehensive Business Process Modeling for Industrial Internet Application 20

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

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20th IEEE International Symposium on Parallel and Distributed Processing with Applications, 12th IEEE International Conference on Big Data and Cloud Computing, 12th IEEE International Conference on Sustainable Computing and Communications and 15th IEEE International Conference on Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2022

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

ISBN: (纸本)9781665464970

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. © 2022 IEEE.

关键词： Modeling languages

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Interactive Web API Recommendation for Mashup Development based on Light Neural Graph Collaborative Filtering 26

Interactive Web API Recommendation for Mashup Development ba...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xiang, Jiayan Chen, Wanjun Wang, Yang Liang, Bowen Liu, Zihao Kang, Guosheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hnust Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350331684

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collaborative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalability. This paper proposes a light neural graph collaborative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalability over the baselines. © 2023 IEEE.

关键词： Collaborative filtering

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Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem 26

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Yang Xiang, Jiayan Cheng, Hangyu Chen, Wanjun Xiao, Yong Kang, Guosheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hnust Xiangtan China

ISBN: (纸本)9798350331684

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline. © 2023 IEEE.

关键词： Ecosystems

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation 22

Multi-view Hypergraph-based Self-supervised Learning Model f...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shen, Jiexun Li, Dongfan Chen, Wanjun Xiao, Yong Kang, Guosheng Liu, Jianxun Peng, Zhenlian 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

ISBN: (纸本)9798331509712

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines. © 2024 IEEE.

关键词： Self-supervised learning

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Crawling and Exploring RESTful Web APIs from RapidAPI 17th

Crawling and Exploring RESTful Web APIs from RapidAPI

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CCF 17th International Conference on Service science, CCF ICSS 2024

作者： Li, Wen Ren, Hongshuai Nie, Yamei Liu, Zihao Kang, Guosheng Liu, Jianxun Peng, Zhenlian 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

ISBN: (纸本)9789819757596

RapidAPI serves as a marketplace and management platform that integrates a wide range of Web APIs, offering developers access to a rich pool of API resources. More and more researchers are delving into ways to maximize the value created by leveraging these resources. Thus, acquiring the Web API data and knowing the details is of significance for researchers and ordinary users. In this paper, we propose the methodology of crawling Web APIs from RapidAPI (https://***/hub) and provide rich visualizations and statistical analysis for the crawled dataset. The crawling code and the crawled dataset are open for public access (https://***/IntelligentServicelab/RESTful-API-Crawler) so that researchers and users can acquire them with no effort. Moreover, the use cases of RESTful Web APIs are provided as a usage reference for users and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web crawler

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