Geneverse：用于基因组和蛋白质组研究的开源多模态大语言模型集合
Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research
AI总结：
提出开源多模态大语言模型集合Geneverse，通过参数高效微调技术适应基因组和蛋白质组研究的三项新任务，表现优于闭源大规模模型。
摘要：
大语言模型（LLMs）的应用在生物医学和医疗保健研究中前景广阔。尽管已有使用广泛生物医学数据训练的开源LLMs，但目前关于LLMs在基因组和蛋白质组学中的应用研究仍然有限。为填补这一空白，我们提出了一组微调的LLMs和多模态大语言模型（MLLMs），称为Geneverse，用于基因组和蛋白质组研究中的三项新任务。Geneverse中的模型基于特定领域的数据集进行训练和评估，我们使用先进的参数高效微调技术来实现模型对任务的适应，包括生成基因功能描述、从蛋白质结构推断蛋白质功能，以及从空间转录组数据中选择标记基因。我们证明，适应后的LLMs和MLLMs在这些任务中表现良好，并且基于我们关注真实性和结构正确性的评估，它们可能优于闭源的大规模模型。我们使用的所有训练策略和基础模型均可免费获取。
英文摘要：
The applications of large language models (LLMs) are promising for biomedical and healthcare research. Despite the availability of open-source LLMs trained using a wide range of biomedical data, current research on the applications of LLMs to genomics and proteomics is still limited. To fill this gap, we propose a collection of finetuned LLMs and multimodal LLMs (MLLMs), known as Geneverse, for three novel tasks in genomic and proteomic research. The models in Geneverse are trained and evaluated based on domain-specific datasets, and we use advanced parameter-efficient finetuning techniques to achieve the model adaptation for tasks including the generation of descriptions for gene functions, protein function inference from its structure, and marker gene selection from spatial transcriptomic data. We demonstrate that adapted LLMs and MLLMs perform well for these tasks and may outperform closed-source large-scale models based on our evaluations focusing on both truthfulness and structural correctness. All of the training strategies and base models we used are freely accessible.
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