{"id":3463,"date":"2024-09-19T06:10:53","date_gmt":"2024-09-19T06:10:53","guid":{"rendered":"https:\/\/cvsc.upcebu.edu.ph\/?p=3463"},"modified":"2026-08-19T06:11:30","modified_gmt":"2026-08-19T06:11:30","slug":"biomarker-identification-for-lung-cancer-using-deep-learning-approaches","status":"publish","type":"post","link":"https:\/\/cvsc.upcebu.edu.ph\/index.php\/2024\/09\/19\/biomarker-identification-for-lung-cancer-using-deep-learning-approaches\/","title":{"rendered":"Biomarker Identification for Lung Cancer Using Deep Learning Approaches"},"content":{"rendered":"

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Lead Researcher(s): <\/strong>Arlan Vincent John V. German & Demelo M. Lao<\/p>\n

Status:<\/strong> Published<\/p>\n

Abstract:<\/strong> Lung cancer is one of the most lethal diseases worldwide. The discovery of its discriminatory biomarkers is crucial to enhance precision diagnosis\/prognosis and lower its early detection costs. The study sampled 12 GEO series from the GEO database containing human gene expressions related to lung cancer diagnosis, sub-typing, prognosis, and biomarker identification. These healthy control and lung cancer samples were used to train, fine-tune, and evaluate the designed \u201cDeepGene\u201d model which performed remarkably (AUC = 0.99) compared to the top-performing baseline model (AUC = 0.98) in classifying the samples accordingly. By utilizing DeepSHAP feature importance with the DeepGene model, a novel biomarker discovery method was proposed allowing for 13 discriminatory biomarkers to be identified; four (4) of which are novel (i.e., CCDC141, and Affymetrix probes 238891_at, AFFX-r2-Bs-dap-3_at, AFFX-ThrX-5_at); while the remaining nine (9) biomarkers are validated by existing studies. These 13 biomarkers attained an AUC = 0.99 based on the testing dataset. For future works, biomarker validation of the four novel biomarkers mentioned is recommended.<\/span><\/span><\/p>\n

Keywords:<\/b><\/p>\n