{"doi":"10.64898/2026.01.28.695053","title":"DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>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 probabilistic interpretability.</jats:p>\n                <jats:p>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.</jats:p>\n                <jats:p>We extracted the probabilistic contribution for each class from the tractable classification model and defined a contribution score for the cancer-cell class. Genetic profiling was then performed based on these scores. The genetic profiling provides insights into which genes or biological pathways are more important for the classification task. Our results show that the model comprehensively utilized information from inherent biological functions encoded by the Nucleotide Transformer as well as statistical gene occurrence counts. These analyses go beyond traditional statistical or gene–expression–level approaches, providing new academic insights in genetic research.</jats:p>","journal":null,"year":2026,"id":6919,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0454,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2026-01-30","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":63388,"name":"Taishi Kusumoto","orcid":null,"position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}