Effective Biological Representation Learning by Masking Gene Expression
The study introduced TxFM, a self-supervised learning model using a masked autoencoder to learn high-fidelity gene expression representations from RNA sequencing data. Trained on a large public corpus, TxFM outperformed larger foundation models by achieving superior performance in predicting gene expression and discovering biological relationships without direct supervision.