Using GPT-4 Prompts to Determine Whether Articles Contain Functional Evidence Supporting or Refuting Variant Pathogenicity
CoRR(2023)
摘要
Purpose: To assess Generative Pre-trained Transformer version 4's (GPT-4)
ability to classify articles containing functional evidence relevant to
assessments of variant pathogenicity.
Results: GPT-4 settings and prompts were trained on a set of 45 articles and
genetic variants. A final test set of 72 manually classified articles and
genetic variants were then processed using two prompts. The prompts asked GPT-4
to supply all functional evidence present in an article for a variant or
indicate that no functional evidence is present. For articles with having
functional evidence, a second prompt asked GPT-4 to classify the evidence into
pathogenic, benign, intermediate, and inconclusive categories. The first prompt
identified articles with variant-level functional evidence with 87% sensitivity
and 89% positive predictive value (PPV). Five of 26 articles with no functional
data were indicated as having functional evidence by GPT-4. For variants with
functional assays present as determined by both manual review and GPT-4, the
sensitivity and PPV of GPT-4 prompt concordance was: Pathogenic (92% sensitive
and 73% PPV), Intermediate or Inconclusive (67% sensitive and 93% PPV), Benign
(100% sensitive and 73% PPV).
Conclusion: The GPT-4 prompts detected the presence or absence of a
functional assay with high sensitivity and PPV, and articles with unambiguous
evidence supporting a benign or pathogenic classification with high sensitivity
and reasonable PPV. Our prompts detected papers with intermediate or
inconclusive evidence with lower sensitivity but high PPV. Our results support
that GPT-4 may be useful in variant classification workflows by enabling
prioritization of articles for review that are likely to have functional
evidence supporting or refuting pathogenicity, but not that GPT-4 is capable of
fully automating the genetics literature review component of variant
classification.
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