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Corrigendum to “Modeling PrOTAC degradation activity with machine learning” [Artificial Intelligence in the Life Sciences 6 (2024) 100104]

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Artificial Intelligence in the Life Sciences 2024年 6卷

作者： Stefano ribes Eva Nittinger Christian Tyrchan rocío Mercado Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg 412 96 Sweden Medicinal Chemistry Research and Early Development Respiratory and Immunology (R&I) BioPharmaceuticals R&D AstraZeneca Pepparedsleden 1 Mölndal 431 83 Sweden

PrOTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. despite their potential, developing new PrOTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PrOTAC data and an open-source deep learning tool for predicting the degradation activity of novel PrOTAC molecules. The curated dataset incorporates important information such as p d C 50 , d m a x , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PrOTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 rOC-AUC, and a test accuracy of 62.3% and 0.604 rOC-AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.

关键词： PrOTAC Machine learning drug discovery Protein degradation

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XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MrI

arXiv

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arXiv 2021年

作者： Zhou, Yirong Qian, Chen Guo, Yi Wang, Zi Wang, Jian Qu, Biao Guo, di You, Yongfu Qu, Xiaobo Biomedical Intelligent Cloud R&D Center Department of Electronic Science National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China Department of Instrumental and Electrical Engineering Xiamen University Xiamen361005 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China China Mobile Group Xiamen361005 China Biomedical Intelligent Cloud R&D Center School of Electronic Science and Engineering Xiamen University Xiamen361005 China

— Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MrI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop an open-access, easy-to-use and high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MrI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative Soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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One for Multiple: Physics-informed Synthetic data Boosts Generalizable deep Learning for Fast MrI reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MrI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although deep Learning (dL) has proven effective for fast MrI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing dL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MrI, called PISF. PISF marks a breakthrough by enabling generalized dL for multi-scenario MrI reconstruction through a single trained model. Our approach separates the reconstruction of a 2d image into many 1d basic problems, commencing with 1d data synthesis to facilitate generalization. We demonstrate that training dL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MrI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MrI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of dL in various fast MrI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MrI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Word Sense disambiguation Method Based on Graph Model and Word Vector

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Tongtong Sun Yongxing du Liyong Zhou Lijing Niu Baoshan Li Yanzhang Gong Jianmin dong School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia China Inner Mongolia Autonomous Region Discipline Inspection and Supervision Big Data Laboratory Baotou Big Data R&D Application Center Baotou Inner Mongolia China Inner Mongolia Autonomous Region Discipline Inspection and Supervision Big Data Laboratory Baotou Big Data R&D Application Center Hohhot Inner Mongolia China

As a basic research of natural language processing, word sense disambiguation (WSd) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.

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Nonadiabatic nuclear-electron dynamics: a quantum computing approach

arXiv

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arXiv 2023年

作者： Kovyrshin, Arseny Skogh, Mårten Tornberg, Lars Broo, Anders Mensa, Stefano Sahin, Emre Symons, Benjamin C.B. Crain, Jason Tavernelli, Ivano Data Science and Modelling Pharmaceutical Sciences R&D AstraZeneca Gothenburg Pepparedsleden 1 MolndalSE-431 83 Sweden Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden The Hartree Centre STFC Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom IBM Research Europe Hartree Centre STFC Laboratory Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom Department of Biochemistry University of Oxford OxfordOX1 3QU United Kingdom IBM Quantum IBM Research Europe – Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland

The combined quantum electron-nuclear dynamics is often associated with the Born–Huang expansion of the molecular wave function and the appearance of nonadiabatic effects as a perturbation. On the other hand, native multicomponent representations of electrons and nuclei also exist, which do not rely on any a priori approximation. However, their implementation is hampered by prohibitive scaling costs and therefore quantum computers offer a unique opportunity for extending their use to larger systems. Here, we propose a quantum algorithm for the simulation of the time-evolution of molecular systems in the second quantization framework, which is applied to the simulation of the proton transfer dynamics in malonaldehyde. After partitioning the dynamics into slow and fast components, we show how the entanglement between the electronic and nuclear degrees of freedom can persist over long times if electrons are not adiabatically following the nuclear displacement. The proposed quantum algorithm may become a valid candidate for the study of electron-nuclear quantum phenomena when sufficiently powerful quantum computers become available. Copyright © 2023, The Authors. All rights reserved.

关键词： Electrons

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A Paired Phase and Magnitude reconstruction for Advanced diffusion-Weighted Imaging

arXiv

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arXiv 2022年

作者： Qian, Chen Wang, Zi Zhang, Xinlin Shi, Boxuan Jiang, Boyu Tao, ran Li, Jing Ge, Yuwei Kang, Taishan Lin, Jianzhong Guo, di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China The United Imaging Healthcare Shanghai China Xingaoyi Medical Equipment Co. Ltd. Yuyao China Department of Radiology Zhongshan Hospital of Xiamen University School of Medicine Xiamen University Xiamen China The School of Computer and Information Engineering Xiamen University of Technology Xiamen China

Objective: Multi-shot interleaved echo planer imaging can obtain diffusion-weighted images (dWI) with high spatial resolution and low distortion, but suffers from ghost artifacts introduced by phase variations between shots. In this work, we aim at solving the challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Methods: An explicit phase model with paired phase and magnitude priors is proposed to regularize the reconstruction (PAIr). The former prior is derived from the smoothness of the shot phase and enforced with low-rankness in the k-space domain. The latter explores similar edges among multi-b-value and multi-direction dWI with weighted total variation in the image domain. results: Extensive simulation and in vivo results show that PAIr can remove ghost artifacts very well under a high number of shots (8 shots) and significantly suppress the noise under the ultra-high b-value (4000 s/mm2). Conclusion: The explicit phase model PAIr with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Significance: PAIr has great potential in advanced clinical dWI applications and brain function research. Copyright © 2022, The Authors. All rights reserved.

关键词： Clinical research

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Adaptive distributed honeypot detection network for enhanced cybersecurity against doS and ddoS attacks

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results in Engineering 2025年 26卷

作者： V. Selva Kumar K.r. Mohan raj S. Gopalakrishnan G. Vennila d. dhinakaran P. Kavitha Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Information Technology Velammal Engineering College Chennai India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering college Chennai India

The increasing prevalence and sophistication of denial of Service (doS) and distributed denial of Service (ddoS) attacks present significant challenges in ensuring the security and stability of modern networked systems. These attacks, characterized by their ability to disrupt services and compromise resources, require innovative and robust detection mechanisms to safeguard highly interactive environments such as honeypot systems. Traditional detection techniques often fall short in addressing the complexities posed by dynamic traffic patterns, diverse attack types, and real-time processing demands. This study introduces the Adaptive distributed Honeypot detection Network (AdHdN), a novel framework that leverages deep learning and probabilistic modeling to address the limitations of existing solutions. AdHdN employs a combination of deep Generative Adversarial Networks (dGANs) and discrete Hidden Markov Models (dHMMs) to achieve superior detection precision across various doS attack types, including application-level, protocol-level, and data volume attacks. Implemented in a highly interactive honeypot environment with distributed server and virtual machine configurations, AdHdN demonstrates remarkable adaptability and resilience. Performance evaluation using the IoTId20 dataset reveals that AdHdN consistently outperforms contemporary models, such as rBMd, BNdH, and AHdL. AdHdN achieves a true positive rate of 99.7% for protocol-level attacks, 99.4% for application-level attacks, and 97.5% for data volume attacks under low attack volumes, maintaining robust performance even as attack intensity scales. These results underscore AdHdN’s potential to redefine doS detection in dynamic and high-interaction environments, offering a scalable and efficient solution to contemporary cybersecurity challenges.

关键词： denial of service distributed denial of service Intrusion detection systems Generative adversarial networks real-time attack detection deep learning

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digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng du, Hongyang Niyato, dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISds). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISds. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (OdOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (rdOP) by leveraging Lyapunov optimization. Then, to solve the rdOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the OdOA, while the simulation results demonstrate that the proposed OdOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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robust Spatial Cell-Type deconvolution with Qualitative reference for Spatial Transcriptomics (Small Methods 5/2025)

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Small Methods 2025年第5期9卷

作者： Qishi dong Yi Yang Ziye Luo Haipeng Shen Xingjie Shi Jin Liu College of Big Data and Internet Shenzhen Technology University Shenzhen 518118 China The Key Laboratory of Developmental Genes and Human Disease School of Life Science and Technology Southeast University Nanjing 210018 China Department of Biometrics Astrazeneca Global R&D (China) CO. Ltd. Shanghai 200085 China Faculty of Business and Economics Hong Kong University Pokfulam Hong Kong SAR China KLATASDS-MOE Academy of Statistics and Interdisciplinary Sciences School of Statistics East China Normal University Shanghai 200062 China School of Data Science The Chinese University of Hong Kong-Shenzhen Shenzhen 518172 China

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Correction: distortion/interaction analysis via machine learning

Digital Discovery

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digital discovery 2025年第3期4卷 879-879页

作者： Samuel G. Espley Samuel S. Allsop david Buttar Simone Tomasi Matthew N. Grayson Department of Chemistry University of Bath Claverton Down Bath BA2 7AY UK M.N.Grayson@bath.ac.uk Data Science and Modelling Pharmaceutical Sciences R&D AstraZeneca Macclesfield UK Chemical Development Pharmaceutical Technology & Development Operations AstraZeneca Macclesfield UK

Correction for ‘distortion/interaction analysis via machine learning’ by Samuel G. Espley et al. , digital discovery , 2024, 3 , 2479–2486, https://***/10.1039/d4dd00224E .

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