How do Large Language Models understand Genes and Cells

Fang, C.; Wang, Y.; Song, Y.; Long, Q.; Lu, W.; Chen, L.; Wang, P.; Feng, G.; Zhou, Y.; Li, X.

doi:10.1101/2024.03.23.586383

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bioRxiv · 10.1101/2024.03.23.586383

How do Large Language Models understand Genes and Cells

Fang, C.·Wang, Y.·Song, Y.·Long, Q.·Lu, W.·Chen, L.·Wang, P.·Feng, G.·Zhou, Y.·Li, X.

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Abstract

Researching genes and their interactions is crucial for deciphering the fundamental laws of biological activity, advancing disease treatment, drug discovery and so on. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains notably scarce. To alleviate this issue, in this paper, we selects seven mainstream LLMs and evaluates their performance across a range of problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, and hold potential for solving real-world problems. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while also being mindful of some challenges associated with their use. We release our code and data at https://github.com/epang-ucas/Evaluate_LLMs_to_Genes. CCS ConceptsApplied computing [->] Recognition of genes and regulatory elements; Bioinformatics; Computational genomics; Computational transcriptomics. ACM Reference FormatChen Fang, Yidong Wang, Yunze Song, Qingqing Long, Wang Lu, Linghui Chen, Pengfei Wang, Guihai Feng, Yuanchun Zhou, and Xin Li. 2024. How do Large Language Models understand Genes and Cells. 1, 1 (March 2024), 14 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

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Fang, C., Wang, Y., Song, Y., Long, Q., Lu, W., Chen, L., Wang, P., Feng, G., Zhou, Y., Li, X.. 2024-03-27. How do Large Language Models understand Genes and Cells. https://doi.org/10.1101/2024.03.23.586383

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