Elsborg, J.; Salvatore, M. Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases. Biomolecules2023, 13, 1516.
Elsborg, J.; Salvatore, M. Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases. Biomolecules 2023, 13, 1516.
Elsborg, J.; Salvatore, M. Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases. Biomolecules2023, 13, 1516.
Elsborg, J.; Salvatore, M. Using LLMs and Explainable ML to Analyze Biomarkers at Single-Cell Level for Improved Understanding of Diseases. Biomolecules 2023, 13, 1516.
Abstract
Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of
the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across
various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To
overcome this, we present a novel approach that employs both an LLM-based framework and explainable
machine learning to facilitate generalization across single-cell datasets and identify gene signatures to
capture disease-driven transcriptional changes.
Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a
symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features
for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility
of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types.
Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals
and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in
bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity
and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to
elucidate underlying molecular disease mechanisms using these models.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
