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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

NTIRE 2021 Challenge on Image Deblurring

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

Evolutionary Cryptography Theory-Based Generating Method for Secure ECs

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Tsinghua Science and Technology 2017年第5期22卷 499-510页

作者： Chao Wang Feng Hu Huanguo Zhang Jie Wu the Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Communication and Information EngineeringShanghai University the Department of Computer and Information Sciences Temple University the Key Lab of Aerospace Information Security and Trusted Computing Wuhan University

Ant Colony Optimization （AGO） has the character of positive feedback, distributed searching, and greedy searching. It is applicable to optimization grouping problems. Traditional cryptographic research is mainly based on pure mathematical methods which have complicated theories and algorithm. It seems that there is no relationship between cryptography and ACO. Actually, some problems in cryptography are due to optimization grouping problems that could be improved using an evolutionary algorithm. Therefore, this paper presents a new method of solving secure curve selection problems using ACO. We improved Complex Multiplication （CM） by combining Evolutionary Cryptography Theory with Weber polynomial solutions. We found that ACO makes full use of valid information generated from factorization and allocates computing resource reasonably. It greatly increases the performance of Weber polynomial solutions. Compared with traditional CM, which can only search one root once time, our new method searches all roots of the polynomial once, and the average time needed to search for one root reduces rapidly. The more roots are searched, the more ECs are obtained.

关键词： ECs Weber polynomial evolutionary algorithm ant algorithm complex multiplication

Simple Method for Realizing Weil Theorem in Secure ECC Generation

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Tsinghua Science and Technology 2017年第5期22卷 511-519页

作者： Feng Hu Chao Wang Huanguo Zhang Jie Wu the Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Communication and Information EngineeringShanghai University the Department of Computer and Information Sciences Temple University the Key Lab of Aerospace Information Security and Trusted Computing Wuhan University

How to quickly compute the number of points on an Elliptic Curve （EC） has been a longstanding challenge. The computational complexity of the algorithm usually employed makes it highly inefficient. Unlike the general EC, a simple method called the Weil theorem can be used to compute the order of an EC characterized by a small prime number, such as the Kobltiz EC characterized by two. The fifteen secure ECs recommended by the National Institute of Standards and Technology （NIST） Digital Signature Standard contain five Koblitz ECs whose maximum base domain reaches 571 bits. Experimental results show that the computation speed decreases for base domains exceeding 600 bits. In this paper, we propose a simple method that combines the Weil theorem with Pascals triangle, which greatly reduces the computational complexity. We have validated the performance of this method for base fields ranging from 2l^100 to 2^1000. Furthermore, this new method can be generalized to any ECs characterized by any small prime number.

关键词： Elliptic Curves （ECs） Pascal＇s triangle Weil theorem

NTIRE 2020 Challenge on Image and Video Deblurring

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

AIM 2019 challenge on real-world image super-resolution: Methods and results

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

Demonstration of 50Gb/s/λ symmetric PAM4 TDM-PON with 10G-class optics and DSP-free ONUs in the O-band

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Demonstration of 50Gb/s/λ symmetric PAM4 TDM-PON with 10G-c...

Optical Fiber communication Conference, OFC 2018

作者： Zhang, Kuo Zhuge, Qunbi Xin, Haiyun Xing, Zhenping Xiang, Meng Fan, Sujie Yi, Lilin Hu, Weisheng Plant, David V. State Key Lab of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering McGill University MontrealQCH3A 2A7 Canada Ciena Corporation OttawaONK2K 0L1 Canada

We demonstrate a 50Gb/s/λ PAM4 TDM-PON based on 10G-class DMLs and PDs in Oband with downlink pre-compensation and uplink post-equalization. Results show that, without any DSP in the ONU, 29dB optical power budget is... 详细信息

ISBN: (纸本)9781943580385

关键词： Budget control

Correction to “Fulcrum: Flexible Network Coding for Heterogeneous Devices”

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IEEE Access 2021年 9卷 108199-108199页

作者： Daniel E. Lucani Morten V. Pedersen Diego Ruano Chres W. Sørensen Frank H. P. Fitzek Janus Heide Olav Geil Vu Nguyen Martin Reisslein Aarhus University Aarhus Denmark Steinwurf ApS Aalborg Denmark IMUVA-Mathematics Research Institute University of Valladolid Valladolid Spain Aalborg University Aalborg Denmark Danish Ministry of Defence Acquisition and Logistics Organization Copenhagen Denmark 5G Lab Germany Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Technische Universität Dresden Dresden Germany School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA

In the above article [1] , in Section II-B.1)a, titled “Outer Encoding,” the first sentence of the second paragraph should be corrected to consistently use $\Omega _{j}$ to denote the coded packets, i.e., this sentence should state: “For systematic outer encoding, the outer encoding vectors for coded packets $\Omega _{j},\,\,j = 1, 2, \ldots, n$ , form an identity matrix of dimension $n \times n$ , i.e., coded packets $\Omega _{j}, j = 1, 2, \ldots, n$ , are identical to the source packets $P_{j}$ .”

关键词： Systematics Network coding Encoding

Towards SDN Based Queuing Delay Estimation

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China communications 2016年第3期13卷 27-36页

作者： MA Haiyan YAN Jinyao Panagiotis Georgopoulos Bernhard Plattner Faculty of Science and Technology Communication University of China Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology Zurich (ETH Zurich)

As one of Qo S(Quality of Service) metrics, delay is critical important to delay sensitive applications, such as interactive video, network game and online surgery. In this paper, we exploit SDN(Software Defined Networking) advantages to solve delay Qo S problem. Our work mainly focuses on SDN based queuing delay estimation with real traffic and end-to-end delay control. First, we propose a queuing estimation model and extended it for end-to-end delay of the whole path. It is proved to be feasible and accurate with experimental results in SDN environment. Second, in order to demonstrate the use of our proposed model, we also implement an end-to-end delay control application in SDN. It fulfills specific delay Qo S requirements by dynamically switching flows to a suitable queue based on estimation results and delay requirements.

关键词： autonomic Qo S dynamic model delay performance SDN