Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models

Pratik Ramprasad, Nidhi Pai, Wei Pan

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial intelligence (AI)/deep learning (DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals, and narrows the performance gap with an elastic net regression approach that uses just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may eventually predict molecular phenotypes from DNA sequences with an accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.

Original languageEnglish (US)
Article number100347
JournalHuman Genetics and Genomics Advances
Volume5
Issue number4
DOIs
StatePublished - Oct 10 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • AI
  • DL
  • Enformer
  • Nucleotide Transformer
  • SNP
  • elastic net regression
  • foundation models
  • gene expression
  • transformers

PubMed: MeSH publication types

  • Journal Article

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