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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Beware of "Explanations" of AI

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

Valley population of donor states in highly strained silicon

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arXiv 2021年

作者： Voisin, B. Ng, K.S.H. Salfi, J. Usman, M. Wong, J.C. Tankasala, A. Johnson, B.C. McCallum, J.C. Hutin, L. Bertrand, B. Vinet, M. Valanoor, N. Simmons, M.Y. Rahman, R. Hollenberg, L.C.L. Rogge, S. Silicon Quantum Computing SydneyNSW2052 Australia School of Physics Sydney The University of New South Wales SydneyNSW2052 Australia Centre for Quantum Computation and Communication Technology School of Physics The University of New South Wales SydneyNSW2052 Australia 5. Physikalisches Institut and Center for Integrated Quantum Science and Technology Universität Stuttgart Stuttgart70569 Germany Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Centre for Quantum Computation and Communication Technology School of Physics The University of Melbourne ParkvilleVIC3010 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3010 Australia School of Materials Science and Engineering The University of New South Wales SydneyNSW2052 Australia Electrical and Computer Engineering Department Purdue University West LafayetteIN United States Quantum Photonics Laboratory School of Engineering RMIT University MelbourneVIC3000 Australia Université Grenoble Alpes CEA LETI Grenoble38000 France

Strain is extensively used to controllably tailor the electronic properties of materials. In the context of indirect band-gap semiconductors such as silicon, strain lifts the valley degeneracy of the six conduction band minima, and by extension the valley states of electrons bound to phosphorus donors. Here, single phosphorus atoms are embedded in an engineered thin layer of silicon strained to 0.8% and their wave function imaged using spatially resolved spectroscopy. A prevalence of the out of plane valleys is confirmed from the real-space images, and a combination of theoretical modelling tools is used to assess how this valley repopulation effect can yield isotropic exchange and tunnel interactions in the xy-plane relevant for atomically precise donor qubit devices. Finally, the residual presence of in-plane valleys is evidenced by a Fourier analysis of both experimental and theoretical images, and atomistic calculations highlight the importance of higher orbital excited states to obtain a precise relationship between valley population and strain. Controlling the valley degree of freedom in engineered strained epilayers provides a new competitive asset for the development of donor-based quantum technologies in silicon. Copyright © 2021, The Authors. All rights reserved.

关键词： Landforms

Meson spectroscopy from spectral densities in lattice gauge theories

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arXiv 2024年

作者： Bennett, Ed Del Debbio, Luigi Forzano, Niccoló Hill, Ryan C. Hong, Deog Ki Hsiao, Ho Lee, Jong-Wan David Lin, C.-J. Lucini, Biagio Lupo, Alessandro Piai, Maurizio Vadacchino, Davide Zierler, Fabian Swansea Academy of Advanced Computing Swansea University Bay Campus Fabian Way SwanseaSA1 8EN United Kingdom Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SwanseaSA2 8PP United Kingdom School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Physics Pusan National University Busan46241 Korea Republic of Extreme Physics Institute Pusan National University Busan46241 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Daejeon34126 Korea Republic of Centre for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Bay Campus Fabian Way SwanseaSA1 8EN United Kingdom Aix-Marseille Université Université de Toulon CNRS CPT Marseille France Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

Spectral densities encode non-perturbative information that enters the calculation of a plethora of physical observables in strongly coupled field theories. Phenomenological applications encompass aspects of standard-model hadronic physics, observable at current colliders, as well as correlation functions characterizing new physics proposals, testable in future experiments. By making use of numerical data produced in a Sp(4) lattice gauge theory with matter transforming in an admixture of fundamental and 2-index antisymmetric representations of the gauge group, we perform a systematic study to demonstrate the effectiveness of recent technological progress in the reconstruction of spectral densities. To this purpose, we write and test new software packages that use energy-smeared spectral densities to analyze the mass spectrum of mesons. We assess the effectiveness of different smearing kernels and optimize the smearing parameters to the characteristics of available lattice ensembles. We generate new ensembles for the theory in consideration, with lattices that have a longer extent in the time direction with respect to the spatial ones. We run our tests on these ensembles, obtaining new results about the spectrum of light mesons and their excitations. We make available our algorithm and software for the extraction of spectral densities, that can be applied to theories with other gauge groups, including the theory of strong interactions (QCD) governing hadronic physics in the standard model. © 2024, CC BY.

关键词： Bosons

A Review on One-Way Concrete Wall Under Axial Loading

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IOP Conference Series: Materials science and engineering 2020年第1期943卷

作者： T Z H Ting M E Rahman H H Lau Department of Civil & Construction Engineering Faculty of Engineering and Science Curtin University 98009 Sarawak Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology 93350 Sarawak Malaysia

As concrete wall panel is gaining popularity in precast construction industry, better insight into its behaviour is of paramount importance. Considerable research has been carried out on concrete wall under axial loading. Most of the published literatures have proposed design equations for wall design purpose. In this aspect, the paper is aimed to review the design equations of one-way concrete wall. The limitations of existing design equations are subsequently identified.

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Enforcing morphological information in fully convolutional networks to improve cell instance segmentation in fluorescence microscopy images

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arXiv 2021年

作者： Zamora-Cárdenas, Willard Mendez, Mauro Calderon-Ramirez, Saul Vargas, Martin Monge, Gerardo Quiros, Steve Elizondo, David Torrents-Barrena, Jordina Molina-Cabello, Miguel A. Computing School Costa Rica Institute of Technology Costa Rica Department of Computer Technology De Montfort University United Kingdom Tropical Diseases Research Center Microbiology Faculty. University of Costa Rica Costa Rica Department of Computer Engineering and Mathematics Rovira i Virgili University Spain Department of Computer Languages and Computer Science University of Malaga Spain Instituto de Investigación Biomédica de Málaga – IBIMA Spain

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT output is subsequently used to train a top-model. The following top-models are considered: a three-class (e.g., foreground, background and cell border) U-net, and a watershed transform. The obtained results suggest a performance boost over traditional U-Net architectures. This opens an interesting research line around the idea of injecting morphological information into a fully convolutional model. © 2021, CC BY.

关键词： Convolutional neural networks

ACOUCOU platform to acquire professional skills and knowledge in the field of acoustics 23

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ACOUCOU platform to acquire professional skills and knowledg...

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Jaruszewska, Karolina Baranski, Filip Piotrowska, Magdalena Melon, Manuel Dazel, Oliver Vorländer, Michael Aspöck, Lukas Horvat, Marko Jambrošic, Kristian Rychtáriková, Monika Kritly, Leopold Herweg, Andreas KFB Acoustics Poland EdTech and SCIENCE Poland LAUM UMR CNRS 6613 Le Mans Université France Institute of Technical Acoustics RWTH Aachen University Germany University Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia KU Leuven Belgium Head Acoustics Germany

ISBN: (纸本)9783939296157

Today, in the digital era, education must meet the needs and adapt to modern forms of knowledge transfer. While this is often implemented in higher education programs (distance learning, OpenCourseWare platforms), vocational education and training seems to be the most traditional branch of education, especially in the field of specialized courses for engineers. Our goal is to create space for new, innovative and multidisciplinary approaches for teaching and e-learning acoustics (e.g., based on gamification, VR, or web simulators). The ACOUCOU Platform (http://***/) _is a part of a strategy aimed at expanding and strengthening acoustic knowledge, supporting the development of innovative teaching methods based on attractive and effective delivery of content, services, teaching methodologies and practices at national and international levels. The form of materials and courses published on the Acoustic Courseware Platform are suitable to be used for self-learning as well as in blended learning, where an educator uses materials from platform to carry out training among employees/students. Technical, professional knowledge is usually presented as text including equations and technical drawings. Presenting it in a visually attractive form is more appealing and increases the motivation of the users. The visualization of phenomena simplifies the understanding of problems and makes it easy to acquire knowledge in similar, practical situations of an engineer's work. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Acoustical engineering Education Industrial Acoustics Noise Awareness Room Acoustics

Design of a secure location based service for mobile cloud applications

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Design of a secure location based service for mobile cloud a...

AHFE 2017 International Conference on Human Factors, Software, and Systems engineering, 2017

作者： Meenakshi Sundaram, K. Kumar, Navjeet Faculty of Computing Botho University Gaborone Botswana Department of Computer Science and Engineering Saveetha School of Engineering Saveetha University Chennai India

ISBN: (纸本)9783319600109

The impact of Location Based Services (LBS) in the mobile cloud paradigm has been huge. The location based services provide a great backbone to mobile cloud and ease the whole process of communication at a global level. Any location based cloud service is used in order to enhance the user’s accessibility and make the service more user-friendly. Using a cloud base location system can improve the quality of the data storage. The objective is creating a location based mobile cloud application in android platform. This application will be able to geo-tag captured images and upload them onto a cloud. The images that are uploaded in the cloud will be sorted according to their locality. The images in the cloud which are in public mode will be visible to a user when he is in the same locality of the picture’s geo tag. © Springer International Publishing AG 2018.

关键词： Location based services