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Gene mining is an important topic in the field of life sciences, buttraditional machine learning methods cannot consider the regulatoryrelationships between genes. Deep learning methods perform poorly in smallsample sizes. This study proposed a deep learning method, calledTransGeneSelector, that can mine critical regulatory genes involved in certainlife processes using a small-sample transcriptome dataset. The method combinesa WGAN-GP data augmentation network, a sample filtering network, and aTransformer classifier network, which successfully classified the state(germinating or dry seeds) of Arabidopsis thaliana seed in a dataset of 79samples, showing performance comparable to that of Random Forests. Further,through the use of SHapley Additive exPlanations method, TransGeneSelectorsuccessfully mined genes involved in seed germination. Through the constructionof gene regulatory networks and the enrichment analysis of KEGG, as well asRT-qPCR quantitative analysis, it was confirmed that these genes are at a moreupstream regulatory level than those Random Forests mined, and the top 11 genesthat were uniquely mined by TransGeneSelector were found to be related to theKAI2 signaling pathway, which is of great regulatory importance forgermination-related genes. This study provides a practical tool for lifescience researchers to mine key genes from transcriptome data.

Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer