DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits

This research provides an XAI-driven genetic profiling approach that may contribute to scientific discoveries in genetic research. We propose a new explainable AI (XAI) classification algorithm that combines probabilistic circuits with the Nucleotide Transformer. By leveraging the strong feature-extraction capability of the Nucleotide Transformer, we design a tractable classification framework based on probabilistic circuits while preserving decomposability and smoothness. To demonstrate the capability of this algorithm, we used the GSE131907 single-cell lung cancer atlas and created a dataset consisting of cancer-cell and normal-cell classes. From each sample, 900 gene types were randomly selected and converted into embedding vectors by the Nucleotide Transformer, after which the classification model was trained. The model demonstrated high representational capacity, achieving an accuracy of 0.97 on the training set and high robustness to unknown genetic contexts with an accuracy of 0.94 on the test set. We extracted the probabilistic contribution for each class from the tractable classification model and defined a contribution score for the cancer-cell class. Gene rankings were then created based on these scores. These rankings may reflect the inherent properties of each gene for the classification task. These analyses go beyond traditional statistical or gene-expression-level approaches, providing new academic insights in genetic research.