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To address this gap, in this article, we select seven mainstream LLMs and evaluate their performance across nine gene-related problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, but still lag behind domain-specific models in comprehending transcriptional expression profiles. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs\u2019 ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while being mindful of the associated challenges. We release our code and data at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/epang-ucas\/Evaluate_LLMs_to_Genes\">https:\/\/github.com\/epang-ucas\/Evaluate_LLMs_to_Genes<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3702234","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T14:02:48Z","timestamp":1730210568000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["How Do Large Language Models Understand Genes and Cells"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6382-1960","authenticated-orcid":false,"given":"Chen","family":"Fang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China and Computer Network Information Center, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9969-8259","authenticated-orcid":false,"given":"Yidong","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4144-0662","authenticated-orcid":false,"given":"Yunze","family":"Song","sequence":"additional","affiliation":[{"name":"University of Liverpool, Liverpool, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7105-361X","authenticated-orcid":false,"given":"Qingqing","family":"Long","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4035-0737","authenticated-orcid":false,"given":"Wang","family":"Lu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4247-4128","authenticated-orcid":false,"given":"Linghui","family":"Chen","sequence":"additional","affiliation":[{"name":"iFLYTEK Research, Hefei, China and Oristruct Biotech Co., Ltd, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-1734","authenticated-orcid":false,"given":"Guihai","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2751-1169","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: Archive for functional genomics data sets\u2013update","volume":"41","author":"Barrett T.","year":"2013","unstructured":"T. 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