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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

Prompting Label Efficiency in Federated Graph Learning Via Personalized Semi-Supervision

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Prompting Label Efficiency in Federated Graph Learning Via P...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qinghua Mao Xi Lin Xiu Su Gaolei Li Lixing Chen Jianhua Li Shanghai Jiao Tong University China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China Big Data Institute Central South University School of Computer Science Faculty of Engineering The University of Sydney Australia

Federated graph learning (FGL) enables the collaborative training of graph neural networks (GNNs) in a distributed manner. A critical challenge in FGL is label deficiency, which becomes more intricate due to non-IID decentralized data. Existing methods have focused on extracting knowledge from abundant unlabeled data, leaving few-shot labeled data unexplored. To this end, we propose ConFGL, a novel FGL framework to enhance label efficiency in federated learning with non-IID subgraphs. We formulate a semi-supervised objective to harness both unlabeled and labeled data, where self-supervised learning is achieved via a graph contrastive module. Additionally, a personalized federated learning (FL) strategy is adopted to concurrently train a global model and an individual model, which helps alleviate the representation disparities encoded by local models. Extensive experiments on four node-level datasets under non-IID settings have shown that ConFGL can consistently provide an average of 4.10% accuracy gains over supervised FL personalization methods while maintaining a higher GPU throughput.

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

Extremal Values of the Atom-Bond Connectivity Index for Trees with Given Roman Domination Numbers

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arXiv 2024年

作者： Ali, Waqar Husin, Mohamad Nazri Bin Nadeem, Muhammad Faisal Special Interest Group on Modeling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan

Consider that G = (X, Y) is a simple, connected graph with X as the vertex set and Y as the edge set. The atom-bond connectivity (ABC) index is a novel topological index that Estrada introduced in Estrada et al. (1998). It is defined as ABC(G) = xy∈XY (G)s ζx +ζxζζyy− 2 where ζx and ζx represent the degrees of the vertices x and y, respectively. In this work, we explore the behavior of the ABC index for tree graphs. We establish both lower and upper bounds for the ABC index, expressed in terms of the graph’s order and its Roman domination number. Additionally, we characterize the tree structures that correspond to these extremal values, offering a deeper understanding of how the Roman domination number (RDN) influences the ABC index in tree graphs. © 2024, CC BY.

关键词： Trees (mathematics)

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

Efficient Extreme Gradient Boosting Based Algorithm for QoS Optimization in Inter-Radio Access Technology Handoffs 4

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Efficient Extreme Gradient Boosting Based Algorithm for QoS ...

4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Al Sibahee, Mustafa A. Ma, Junchao Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq Tom Mboya University College Faculty of Biological & Physical Sciences Homabay 199-40300 Kenya College of Education for Pure Sciences Department of Computer Science University of Basrah Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen430074 China

ISBN: (纸本)9781665468350

The deployment of many base stations within a small network coverage area can potentially increase network capacities. However, this implies frequent handoffs as the users move within the small tracking areas. An effective hand off strategy is therefore required to boost quality of service and quality of experience during the handoff process. Unfortunately, most of the conventional handoff strategies are reactive and tend to deploy single or few parameters as inputs to the decision-making process. As such, handoffs are executed without fully considering user satisfaction as well as application, device and service requirements. This results in deterioration of both quality of service and quality of experience after the handoff procedures. In this paper, extreme gradient boosting based algorithm is presented. The results show that it has minimal handoff failure and ping pong rates, while exhibiting very high handoff success rates. In addition, its receiver operating characteristic-area under curve value of 0.97627 exceeds the 0.7 threshold. Consequently, it is capable of predicting the success of the inter-radio access technology handoff with high probability. © 2022 IEEE.

关键词： Quality of service

Feature Selection Approaches for Optimising Music Emotion Recognition Methods

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arXiv 2022年

作者： Cai, Le Ferguson, Sam Lu, Haiyan Fang, Gengfa School of Electrical and Data Engineering School of Computer Science Faculty of Engineering & IT University of Technology Sydney SydneyNSW2007 Australia

The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion;however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task. © 2022, CC BY.

关键词： Emotion Recognition

Autonomous LLM-driven research from data to human-verifiable research papers

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arXiv 2024年

作者： Ifargan, Tal Hafner, Lukas Kern, Maor Alcalay, Ori Kishony, Roy Faculty of Data and Decision Sciences Technion–Israel Institute of Technology Haifa Israel Faculty of Biology Technion–Israel Institute of Technology Haifa Israel Epsio Tel Aviv Israel Faculty of Computer Science Technion–Israel Institute of Technology Haifa Israel Faculty of Biomedical Engineering Technion–Israel Institute of Technology Haifa Israel

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability. Copyright © 2024, The Authors. All rights reserved.

关键词： Transparency

Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration

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Cloud-Based Oxygen Management System with Seamless HTTP Prot...

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Hemalatha Jayabharathi Ramasamy K. Selvi P. Anandan R. Meenakshi S. P. Maniraj Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Faculty of Management Studies S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management.

关键词： Cloud computing Hospitals Process control User interfaces Control systems HTTP Real-time systems