{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:02:03Z","timestamp":1780531323550,"version":"3.54.1"},"reference-count":17,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Bioinform Biol Insights"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>Prompt and accurate identification of Listeria monocytogenes at the whole-genome level is essential for food safety surveillance and outbreak prevention, yet existing culture-based, PCR, and next-generation sequencing (NGS) workflows are either slow, labor-intensive, or rely on opaque machine learning models with limited interpretability. This study proposes an explainable genomic classification framework that couples transformer-based DNA embeddings with gradient boosting to distinguish Listeria from non-Listeria genomes. A curated dataset of 700 complete bacterial genomes (350 L. monocytogenes and 350 biologically related non-Listeria genomes) was assembled from the NCBI Assembly database and rigorously filtered to retain high-quality assemblies. Each genome was tokenized into overlapping 6-mers and encoded using the pretrained DNABERT model to obtain contextual genome-level embeddings, which were then classified with a LightGBM classifier. Model decisions were interpreted using SHapley Additive exPlanations (SHAP) to quantify the contribution of individual embedding dimensions and associated k-mer patterns to each prediction. After final verification of the confusion matrix, the proposed DNABERT + LightGBM + SHAP pipeline correctly classified 335\/350 Listeria genomes and 330\/350 non-Listeria genomes, corresponding to 665\/700 correct classifications and a corrected accuracy of 95.00%. The same evaluation yielded precision of 94.37%, recall of 95.71%, F1-score of 95.03%, and an AUC of 0.9976. Comparative experiments show that the framework consistently outperforms conventional k-mer-based Random Forest, TF-IDF + SVM, CNN, XGBoost, and DNABERT + Logistic Regression baselines across all metrics. Beyond its strong predictive performance, SHAP-based analysis reveals discriminative sequence patterns that may correspond to putative genomic signatures of Listeria. Overall, the proposed approach provides a high-performance, interpretable tool for genome-scale Listeria identification and offers a transferable template for explainable pathogen genomics in food safety and public health applications.<\/jats:p>","DOI":"10.1177\/11779322261457840","type":"journal-article","created":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T10:37:01Z","timestamp":1780223821000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Listeria Genome Identification Using DNABERT Embedding With LightGBM and SHAP-Based Explainable Classification"],"prefix":"10.1177","volume":"20","author":[{"given":"Sajeev Ram","family":"Arumugam","sequence":"first","affiliation":[{"name":"Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ananth J.","family":"P","sequence":"additional","affiliation":[{"name":"Dayananda Sagar University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sankar Ganesh","family":"Karuppasamy","sequence":"additional","affiliation":[{"name":"Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. Senthil","family":"Murugan","sequence":"additional","affiliation":[{"name":"Chettinad Academy of Research and Education"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-0716","authenticated-orcid":false,"given":"Wulfran Fendzi","family":"Mbasso","sequence":"additional","affiliation":[{"name":"University of Douala"},{"name":"Saveetha Institute of Medical and Technical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ambe","family":"Harrison","sequence":"additional","affiliation":[{"name":"Applied Science Private University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,31]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/1541-4337.13348"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfoodmicro.2023.110491"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pwat.0000291"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lwt.2024.116108"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1099\/jmm.0.001641"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/antibiotics13050447"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2023.1331233"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3778"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2024.139527"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-025-01796-y"},{"key":"e_1_3_6_12_2","unstructured":"National Center for Biotechnology Information (NCBI). The NCBI Genome Assembly Data Model. NCBI Datasets Documentation. National Library of Medicine National Institutes of Health. Available from: https:\/\/www.ncbi.nlm.nih.gov\/datasets\/docs\/v2\/data-processing\/policies-annotation\/data-model\/"},{"key":"e_1_3_6_13_2","unstructured":"Kassab D. DNABERT a linguistic approach for sequential predictions within Biology and Health; Groningen: University of Groningen Faculty of Science and Engineering; 2024."},{"key":"e_1_3_6_14_2","volume-title":"DNABERT Reveals TF Motifs Information in DNA with Attention","author":"Zhao Z","year":"2025","unstructured":"Zhao Z. DNABERT Reveals TF Motifs Information in DNA with Attention. New Haven, CT: Yale University; 2025."},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11947-025-03819-4"},{"key":"e_1_3_6_16_2","doi-asserted-by":"crossref","unstructured":"Zar A Zar L Mohsen S Magdi Y Zughaier SM. A comprehensive review of algorithms developed for rapid pathogen detection and surveillance. In: Chowdhury MEH Kiranyaz S editors. Surveillance Prevention and Control of Infectious Diseases: An AI Perspective. 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