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

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Sun, Yingying, A, Jun, 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, E, Weinan, 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
Formato:	Preprint
Publicado:	2025
Materias:	Other Quantitative Biology Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

_version_	1866916625554341888
author	Sun, Yingying A, Jun 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 E, Weinan 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
author_facet	Sun, Yingying A, Jun 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 E, Weinan 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
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_15867
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence Sun, Yingying A, Jun 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 E, Weinan 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 Other Quantitative Biology Artificial Intelligence 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.
title	Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
topic	Other Quantitative Biology Artificial Intelligence
url	https://arxiv.org/abs/2502.15867