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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Dehui Wei Qipeng Xie Xia Li Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University IoT Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science ETH Zurich Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers.

关键词： Accuracy High dimensional data Input variables High performance computing Instruments Noise Genomics Feature extraction Bioinformatics Optimization

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Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models

arXiv

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arXiv 2025年

作者： Barman, Kristian G. Caron, Sascha Sullivan, Emily de Regt, Henk W. de Austri, Roberto Ruiz Boon, Mieke Färber, Michael Fröse, Stefan Hasibi, Faegheh Ipp, Andreas Kapoor, Rukshak Kasieczka, Gregor Kostić, Daniel Krämer, Michael Golling, Tobias Lopez, Luis G. Marco, Jesus Otten, Sydney Pawlowski, Pawel Vischia, Pietro Weber, Erik Weniger, Christoph CLPS - Centre for Logic and Philosophy of Science UGent Belgium IMAPP Radboud University and Nikhef NL Netherlands Utrecht University Netherlands Institute for Science in Society Radboud University Netherlands CSIC-UV Spain University of Twente Netherlands TU Dresden & ScaDS.AI Germany ErUM-Data-Hub & TU Dortmund University Germany Computing and Information Science Radboud University Netherlands TU Wien Austria Patiala India Universität Hamburg Germany Institute of Philosophy University of Leiden Netherlands RWTH Aachen University Germany University of Geneva Switzerland Munich Center for Mathematical Philosophy LMU Munich Germany CSIC-UC Spain Radboud University Netherlands Ippen Digital Germany Universidad de Oviedo ICTEA Spain GRAPPA Institute of Physics University of Amsterdam Netherlands

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we refer to as Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models. © 2025, CC BY.

关键词： Benchmarking

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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The networks of ingredient combination in cuisines around the world

arXiv

引用

arXiv 2024年

作者： Caprioli, Claudio Kulkarni, Saumitra Battiston, Federico Iacopini, Iacopo Santoro, Andrea Latora, Vito Department of Physics and Astronomy University of Catania Catania95125 Italy Department of Scientific Computing Modeling & Simulation Savitribai Phule Pune University Pune411007 India Department of Network and Data Science Central European University Vienna1100 Austria Network Science Institute Northeastern University London LondonE1W 1LP United Kingdom Department of Physics Northeastern University BostonMA02115 United States Neuro-X Institute École Polytechnique Fédérale de Lausanne Geneva1202 Switzerland School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom Complexity Science Hub Vienna1080 Austria

Investigating how different ingredients are combined in popular dishes is crucial to reveal the fundamental principles behind the formation of food preferences. Here, we use data from food repositories and network analysis to characterize worldwide cuisines. In our framework, each cuisine is represented as a network, where nodes correspond to ingredient types and weighted links describe how frequently pairs of ingredient types appear together in recipes. The networks of ingredient combinations reveal cuisine-specific patterns, highlighting similarities and differences in gastronomic preferences across different world regions. We find that popular ingredients, recurrent combinations, and the way they are organized within the backbone of the network provide a unique fingerprint for each cuisine. Hence, we demonstrate that networks of ingredient combinations are able to cluster global cuisines into meaningful geo-cultural groups, and can also be used to train models to uniquely identify a cuisine from a subset of its recipes. Our study advances our understanding of food combinations and helps uncover the geography of taste, paving the way for the creation of new and innovative recipes. Copyright © 2024, The Authors. All rights reserved.

关键词： Food ingredients

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Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

arXiv

引用

arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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MANDERA: Malicious Node Detection in Federated Learning via Ranking

arXiv

引用

arXiv 2021年

作者： Zhu, Wanchuang Zhao, Benjamin Zi Hao Luo, Simon Liu, Tongliang Deng, Ke Data61 CSIRO Sydney Australia Department of Computing Macquarie University Australia. Optus Macquarie Cyber Security Hub Sydney Australia School of Computer Science The University of New South Wales Sydney Australia School of Computer Science The University of Sydney Sydney Australia Center for Statistical Science Department of Industrial Engineering Tsinghua University Beijing China

Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly). To address the above, for the first time, we propose MANDERA which is theoretically guaranteed to efficiently detect all malicious gradients under Byzantine attacks with no prior knowledge or history about the number of attacked nodes. More specifically, we transfer the original updating gradient space into a ranking matrix. By such an operation, the scales of different dimensions of the gradients in the ranking space become identical. The high-dimensional benign gradients and the malicious gradients can be easily separated. The effectiveness of MANDERA is further confirmed by experimentation on four Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping, Shifted Mean), comparing with state-of-the-art defenses. The experiments cover both IID and Non-IID datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning algorithms

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The Vertebrate Breed Ontology: Towards effective breed data standardization

arXiv

引用

arXiv 2024年

作者： Mullen, Kathleen R. Tammen, Imke Matentzoglu, Nicolas A. Mather, Marius Balhoff, James P. Esdaile, Elizabeth Leroy, Gregoire Park, Carissa A. Rando, Halie M. Saklou, Nadia T. Webb, Tracy L. Vasilevsky, Nicole A. Mungall, Christopher J. Haendel, Melissa A. Nicholas, Frank W. Toro, Sabrina Department of Genetics University of North Carolina at Chapel Hill Chapel HillNC United States Sydney School of Veterinary Science The University of Sydney SydneyNSW Australia Semanticly Ltd Athens Greece Sydney Informatics Hub The University of Sydney SydneyNSW Australia Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel HillNC United States Veterinary Genetics Laboratory School of Veterinary Medicine University of California DavisCA United States Department of Clinical Sciences Colorado State University Fort CollinsCO United States Animal Production and Health Division Food and Agriculture Organization of the United Nations Rome Italy Department of Animal Science Iowa State University AmesIA United States Department of Computer Science Smith College NorthamptonMA United States Data Collaboration Center Critical Path Institute TucsonAZ United States Lawrence Berkeley National Laboratory BerkeleyCA United States

Background - Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison;this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Hypothesis/Objectives - A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals - No live animals were used. Methods - The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence–readiness. Results - VBO is an open, community-driven ontology representing over 19,500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed’s genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance - The adoption of VBO as a source of standard breed names in databases and veterinary electronic health records can enhance veterinary data interoperability and computability. © 2024, CC BY.

关键词： Electronic health record

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Success-Driven Opinion Formation Determines Social Tensions

SSRN

引用

SSRN 2023年

作者： Chica, Manuel Perc, Matjaž Santos, Francisco C. Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Instituto Superior Técnico Universidade de Lisboa Porto Salvo2744-016 Portugal Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 Maribor2000 Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Alma Mater Europaea Slovenska ulica 17 Maribor2000 Slovenia Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Korea Republic of

Polarization is common in politics and public opinion. It is believed to be shaped by the media as well as ideologies, and often incited by misinformation. However, little is known about the microscopic dynamics behind polarization and the resulting social tensions. By coupling together opinion formation with the strategy selection in different social dilemmas, we reveal how success at an individual level transforms to global consensus or lack thereof. When defection carries with it the fear of punishment in the absence of greed, as in the stag-hunt game, opinion fragmentation is smallest. Conversely, if defection promises a higher payoff and thus evokes also greed, like in the prisoner's dilemma and the snowdrift game, consensus is much more difficult to attain. Our research thus challenges the top-down narrative of social tensions, showing that they may well originate from fundamental principles at individual level, like the desire to prevail in pairwise evolutionary comparisons. © 2023, The Authors. All rights reserved.

关键词： Polarization

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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The Vertebrate Breed Ontology: Toward Effective Breed data Standardization

引用

Journal of veterinary internal medicine 2025年第4期39卷 e70133页

作者： Kathleen R Mullen Imke Tammen Nicolas A Matentzoglu Marius Mather James P Balhoff Elizabeth Esdaile Gregoire Leroy Carissa A Park Halie M Rando Nadia T Saklou Tracy L Webb Nicole A Vasilevsky Christopher J Mungall Melissa A Haendel Frank W Nicholas Sabrina Toro Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Sydney School of Veterinary Science The University of Sydney Sydney New South Wales Australia. Semanticly Ltd Athens Greece. Sydney Informatics Hub The University of Sydney Sydney New South Wales Australia. Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Veterinary Genetics Laboratory School of Veterinary Medicine University of California Davis California USA. Animal Production and Health Division Food and Agriculture Organization of the United Rome Italy. Department of Animal Science Iowa State University Ames Iowa USA. Department of Computer Science Smith College Northampton Massachusetts USA. Department of Clinical Sciences Colorado State University Fort Collins Colorado USA. Data Collaboration Center Critical Path Institute Tucson Arizona USA. Lawrence Berkeley National Laboratory Berkeley California USA.

BACKGROUND:Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. HYPOTHESIS/OBJECTIVES:A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. ANIMALS:No live animals were used. METHODS:The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. RESULTS:VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. CONCLUSION AND CLINICAL IMPORTANCE:The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.

关键词： animal population groups biological ontologies breeding genetics precision medicine theriogenology

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