Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning

We used publicly available datasets as detailed in the paper, which can be provided upon request.

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Publication Details

Journal
Bioinformatics Advances
Published
2024-12-26
DOI
https://doi.org/10.1093/bioadv/vbaf019
Citations
10
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
3.47
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article

Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning

Erdal Coşgun, Shuangjia Lu
10 citations
Bioinformatics Advances
Explainable Artificial Intelligence (XAI)
3.47
article

Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning

Erdal Coşgun, Shuangjia Lu
article en
10 citations

Abstract

We used publicly available datasets as detailed in the paper, which can be provided upon request.

Bioinformatics AdvancesVol. 5(1)
Microsoft (United States) (US), Yale University (US)
Openalex Percentile: Top 6%
Explainable Artificial Intelligence (XAI)
3.47
DOIOpen Access PDFOpenAlex
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Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning — Erdal Coşgun, Shuangjia Lu · Bioinformatics Advances (2024) | TGRS Research Map | TGRS