For gene expression analysis, a smaller, carefully curated dataset with the right model architecture beats massive datasets—showing that thoughtful data selection and design choices are critical for biological AI, not just scale.
This paper develops TxFM, a self-supervised learning model for analyzing gene expression data from RNA sequencing. Using masked autoencoding (hiding parts of data to learn patterns), the model learns useful representations of genes that outperform larger foundation models.