{"id":"W4405004943","doi":"10.3390/ijms252313029","title":"Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status","year":2024,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Breast cancer; Estrogen receptor; Metabolomics; Machine learning; Computational biology; Artificial intelligence; Computer science; Bioinformatics; Cancer; Medicine; Internal medicine; Biology","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":[0.0009861116,0.0007452125,0.0005957394,0.0007771234,0.0001648103,0.0006046494,0.0003511184,0.000487702,0.0006023335],"category_scores_gemma":[0.002074429,0.000187057,0.0005604203,0.0004643857,0.0001265398,0.0004874792,0.0002638311,0.0005318743,0.0005592862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817114,"about_ca_system_score_gemma":0.0005443629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004922879,"about_ca_topic_score_gemma":0.004794089,"domain_scores_codex":[0.999796,0.00006834784,0.00001205923,0.00005496903,0.00003981512,0.00002891842],"domain_scores_gemma":[0.9994228,0.0003620643,0.00006784737,0.00002995732,0.0001036411,0.00001375636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000559463,0.0003941742,0.05526828,0.0001411649,0.0002899826,0.0001313391,0.00003261634,0.7471861,0.01048245,0.0007369581,0.002752597,0.1820249],"study_design_scores_gemma":[0.000007354035,0.0000444065,0.004611701,0.000007521196,0.00001849772,0.00002362968,0.000007212063,0.9931434,0.001304305,0.0005185804,0.0003070264,0.00000631528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896974,0.003786966,0.2973391,0.0008258375,0.0001006494,0.0001185225,0.001918383,0.002814869,0.003398301],"genre_scores_gemma":[0.9659521,0.000445892,0.03133048,0.00007457403,0.00003047464,0.00004137632,0.001130041,0.00003203033,0.0009630072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004922879,"threshold_uncertainty_score":0.009788454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02624592488021442,"score_gpt":0.3090525728800567,"score_spread":0.2828066479998423,"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."}}