AI-driven transcriptomic encoders: From explainable models to accurate, sample-independent cancer diagnostics

In the rapidly evolving domain of medical technology, the utilization of sophisticated algorithms for deciphering transcriptional data has emerged as a critical aspect, especially in the oncology sector. These algorithms, drawing upon methodologies from fields such as natural language processing and advanced image analysis, can significantly enhance the accuracy in predicting cancer-related molecular states. Notably, Transformer models, renowned for their proficiency in handling extensive datasets, are now being adapted for breakthroughs in medical diagnostics or in stratifying patients according to prognostic levels. Our study contributes to the field of precision medicine by integrating Transformer-based learning, exemplified by the Geneformer model, with explainable AI techniques. These techniques are employed to find out the input variables (genes resulting from genomic transcription) most correlated with the decisions of neural network systems. This insight, a key goal in genomic research, aims to select the most relevant gene subset for each specific task in which a neural network is employed. This selection approach has proven to be effective in two classification tasks: cell type classification and breast cancer type classification. Such effectiveness has been demonstrated even across various cohorts of patients. When applying Geneformer-like architecture analyses solely to the selected gene subsets, the outcomes either maintain their accuracy or significantly improve. This approach, aims not only to contribute to the identification of vital genetic markers in cancer genomics, but also to exemplify the adaptability of AI models to different datasets, marking a significant step towards the development of accurate and universally applicable diagnostic tools for precision medicine. • Integrated Transformer-based learning with Explainable AI for cancer detection. • Identified key gene subsets for precise genomic analysis. • Maintained high accuracy across diverse patient cohorts. • Reduced model reliance on specific data encoding techniques.