{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004760201,0.0002028043,0.0003408854,0.0002042449,0.0001562903,0.00003549267,0.000180365,0.00008271345,0.000004313742],"category_scores_gemma":[0.0007952077,0.0001682062,0.0001030527,0.0004064114,0.00009863017,0.00001101615,0.00009768959,0.00008695661,3.599784e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001632501,"about_ca_system_score_gemma":0.00006688121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006090401,"about_ca_topic_score_gemma":0.00004098087,"domain_scores_codex":[0.9987375,0.00004862337,0.0004681908,0.0004053465,0.00008798039,0.0002524095],"domain_scores_gemma":[0.9990563,0.00007765586,0.0002571776,0.0002566226,0.0003223362,0.00002989035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003805536,0.0001178436,0.009247018,0.00009709082,0.000140326,2.193535e-7,0.00004181108,0.00003966733,0.9638975,0.004007532,0.00002014728,0.02201032],"study_design_scores_gemma":[0.0002458592,0.0001667983,0.001253808,0.00005249092,0.0001023578,0.000001334112,0.0003588495,0.00219486,0.9816635,0.0003657075,0.01342334,0.0001710558],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.776179,0.009887744,0.2128401,0.0001078223,0.0001237879,0.0006376158,0.00008082843,0.00003337539,0.000109738],"genre_scores_gemma":[0.9760756,0.000952423,0.02178446,0.00003361898,0.0000542967,0.0006046631,0.0001234588,0.00002157013,0.000349919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1998966,"threshold_uncertainty_score":0.6859248,"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."}}