{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang X"],"funding":["National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["830-845"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11615217"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["34(12)"],"pubmed_abstract":["Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively inte"],"journal":["Cell research"],"pubmed_title":["GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model."],"pmcid":["PMC11615217"],"funding_grant_id":["No.31971289","No.62202455","No.91954201","No.32341013"],"pubmed_authors":["X-Compass Consortium","Hu Y","Shen Q","Hu Z","Zhang J","Wang C","Liang Z","Lin X","Fang C","Wang H","Guo S","Zhang S","Gao F","Wang J","Zhang R","Wu H","Wang P","Guo J","Dong K","Dong J","Wang T","Deng Z","Chen Y","Wang X","Chen S","Wang Y","Wu W","Zhang C","Wang Z","Gu Q","Man Z","Li H","Yang G","Chen Q","He C","Li C","Xu P","Li F","He H","Hu B","Yang X","Li W","Li X","Li Y","Yang Z","Li Z","Li S","He X","Yang Q","Zhong L","Xu Z","Jiang J","Liu J","Yu L","Han L","Qin J","Jiang H","Ren Y","Bu D","Miao H","Qin X","Feng G","Jiang S","Meng Z","Yu J","Long Q","Zhao Y","Liu W","Liu Y","Zou Y","Zou Z","Liu Z","Zhai W","Qi X","Li A","Zhou J","Zong S","Cui L","Cui W","Zhou Y","Luo Q","Xiao M","Huang Y","Zhang Z","Zhang Y","Huang W","Tian Y","Luo Y","Ma K","Liu G","Xin Y"],"additional_accession":[]},"is_claimable":false,"name":"GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.","description":"Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively inte","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-04T01:15:29.564Z","creation":"2025-04-04T01:15:29.564Z"},"accession":"S-EPMC11615217","cross_references":{"pubmed":["39375485"],"doi":["10.1038/s41422-024-01034-y"]}}