Current opinions on large cellular models  

作　　者：Minsheng Hao Lei Wei Fan Yang Jianhua Yao Christina V.Theodoris Bo Wang Xin Li Ge Yang Xuegong Zhang 

机构地区：[1]MOE Key Laboratory of Bioinformatics and Bioinformatics Division,BNRIST,Department of Automation,Tsinghua University,Beijing,China [2]AI Lab,Tencent,Shenzhen,China [3]Gladstone Institutes and University of California,San Francisco,California,USA [4]Department of Computer Science,University of Toronto,Toronto,Ontario,Canada [5]State Key Laboratory of Stem Cell and Reproductive Biology,Institute of Zoology,Chinese Academy of Sciences,Beijing,China [6]State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,China [7]School of Life Sciences and School of Medicine,Center for Synthetic and Systems Biology,Tsinghua University,Beijing,China

出　　处：《Quantitative Biology》2024年第4期433-443,共11页定量生物学（英文版）

基　　金：National Natural Science Foundation of China,Grant/Award Number:62250005;National Key Research and Development Program of China,Grant/Award Number:2021YFF1200900。

摘　　要：1|INTRODUCTION.Large language models(LLMs)have made breakthroughs in natural language processing(NLP)and understanding,and have brought revolutions in many other fields[1-4].Inspired by those successes,several large cellular models(LCMs)adopting similar structures of LLMs have been developed for single-cell transcriptomics,including(but not limited to)scBERT[5],Geneformer[6],scGPT[7],scFoundation[8],and GeneCompass[9].The practices of these models have shown LCMs’power and potential in various biological tasks and illustrated the possibilities of revolutionizing future biological studies by LCMs.

关 键 词：large cellular models large language models scBERT Geneformer scGPT scFoundation GeneCompass single-cell transcriptomics 

分 类 号：Q811.4[生物学—生物工程] TP18[自动化与计算机技术—控制理论与控制工程]