This research paper explores using pre-trained transformers, a type of deep learning model, to improve predictions of cancer treatment efficacy and identify associated biomarkers. The authors introduce the Clinical Transformer, a framework that leverages large datasets to predict survival outcomes more accurately than traditional methods. The study highlights the model's ability to handle diverse data, including sparse features and missing data, and its explainability module enables researchers to understand the factors driving predictions. Furthermore, the generative capabilities of the Clinical Transformer allow for perturbation analysis, which can shed light on potential treatment responses and resistance mechanisms.

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