2025/01/01 by Dailin Gan, Jun Li · 1 voice
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Genomics and Phylogenetic Studies
paper · pdf · doi:10.1016/j.csbj.2025.07.053
openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
While foundation transformer-based models developed for gene expression data analysis can be costly to train and operate, a recent approach known as GenePT offers a low-cost and highly efficient alternative. GenePT utilizes OpenAI's text-embedding function to encode background information, which is in textual form, about genes. However, the closed-source, online nature of OpenAI's text-embedding service raises concerns regarding data privacy, among other issues. In this paper, we explore the possibility of replacing OpenAI's models with open-source transformer-based text-embedding models. We identified ten models from Hugging Face that are small in size, easy to install, and light in computation. Across all four gene classification tasks we considered, some of these models have outperformed OpenAI's, demonstrating their potential as viable, or even superior, alternatives. Additionally, we find that fine-tuning these models often does not lead to significant improvements in performance.