{"id":"W4415821888","doi":"10.3390/metabo15110716","title":"Identification of a Novel Lipidomic Biomarker for Hepatocyte Carcinoma Diagnosis: Advanced Boosting Machine Learning Techniques Integrated with Explainable Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Princess Nourah Bint Abdulrahman University","keywords":"Lipidomics; Hepatocellular carcinoma; Biomarker; Boosting (machine learning); Sphingolipid; Sphingomyelin; Biomarker discovery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002162602,0.0006321652,0.0006848929,0.001097121,0.0001789955,0.0006474202,0.0003533037,0.0005293021,0.0005890751],"category_scores_gemma":[0.002589063,0.0002134031,0.000781069,0.000487815,0.0002753413,0.0004406846,0.0005052292,0.0005891077,0.0002424443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004160982,"about_ca_system_score_gemma":0.0004656767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000347418,"about_ca_topic_score_gemma":0.0004421017,"domain_scores_codex":[0.9996217,0.0001820858,0.00001778666,0.0000681268,0.00007631062,0.00003395586],"domain_scores_gemma":[0.9992894,0.0003379752,0.0001384193,0.00006526885,0.000136849,0.00003199267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0011127,0.0006971278,0.1969035,0.0004842288,0.0008562491,0.0004655714,0.0001664675,0.237503,0.1434874,0.004457265,0.001637368,0.4122291],"study_design_scores_gemma":[0.00003917261,0.0004558585,0.03187533,0.0000273663,0.0002013665,0.0002349481,0.00002655842,0.9326866,0.02724768,0.00567291,0.001497276,0.00003497507],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4924932,0.002267177,0.5019166,0.00076141,0.00007050644,0.000150695,0.0003448064,0.0006539602,0.001341594],"genre_scores_gemma":[0.9133711,0.0004343314,0.08542214,0.00009900394,0.0000473287,0.00006003553,0.0002309371,0.00001910954,0.000315973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002162602,"threshold_uncertainty_score":0.01143706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185749131723619,"score_gpt":0.2801620762836145,"score_spread":0.2615871631112526,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}