Semantic R-CNN for Natural Language Object Detection

Ye, Shuxiong; Qin, Zheng; Xu, Kaiping; Huang, Kai; Wang, Guolong

doi:10.1007/978-3-319-77383-4_10

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…This approach allows for more nuanced and dynamic representations. For example, Hou and Ji [21] employed ChatGPT for cell type annotation; Wysocki et al [22] investigated biomedical meanings encoded by BioBERT and BioMegatron embeddings; and Ye et al [23] utilized instruction fine-tuning to achieve competitive results on graph data task benchmarks with an LLM. Compared to prior works that directly query LLMs for biological tasks, our method solely utilizes the input descriptions of each gene (which can be sourced from high-quality databases such as NCBI [24]) and the embedding model of LLMs, which suffers less from problems such as hallucination.…”

Section: Using Language Models For Cell Biologymentioning

confidence: 99%

GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT

Chen,

Zou

2023

Preprint

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There has been significant recent progress in leveraging large-scale gene expression data to develop foundation models for single-cell transcriptomes such as Geneformer [1], scGPT [2], and scBERT [3]. These models infer gene functions and interrelations from the gene expression profiles of millions of cells, which requires extensive data curation and resource-intensive training. Here, we explore a much simpler alternative by leveraging ChatGPT embeddings of genes based on literature. Our proposal, GenePT, uses NCBI text descriptions of individual genes with GPT-3.5 to generate gene embeddings. From there, GenePT generates single-cell embeddings in two ways: (i) by averaging the gene embeddings, weighted by each gene's expression level; or (ii) by creating a sentence embedding for each cell, using gene names ordered by the expression level. Without the need for dataset curation and additional pretraining, GenePT is efficient and easy to use. On many downstream tasks used to evaluate recent single-cell foundation models --- e.g., classifying gene properties and cell types --- GenePT achieves comparable, and often better, performance than Geneformer and other methods. GenePT demonstrates that large language model embedding of literature is a simple and effective path for biological foundation models.

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Section: Using Language Models For Cell Biologymentioning

confidence: 99%