Fig. 5: Performance of GPT4o-mini with few-shots learning in pooled-data evaluation using primary single-cell data. | Nature Communications

Fig. 5: Performance of GPT4o-mini with few-shots learning in pooled-data evaluation using primary single-cell data.

From: scDrugMap: benchmarking large foundation models for drug response prediction

Fig. 5: Performance of GPT4o-mini with few-shots learning in pooled-data evaluation using primary single-cell data.

A Prompt display used for GPT4o-mini. For the prompt word template, we first use a technique like the thought chain to prompt the model how it should think about the output, then tell the model the data source and sequence information, and finally we repeat telling the model and give an output template to ensure the consistent of the output format. A complete input example with prompt is also showed. b Radar plots showing the mean F1 scores of GPT4o-mini in predicting drug response across different tissue, drug types, and cancer types (the radial axis is scaled from 0–1). Source data are provided as a Source Data file.

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