{"id":"W2942364178","doi":"10.1016/j.ygeno.2019.04.017","title":"Comprehensive expression-based isoform biomarkers predictive of drug responses based on isoform co-expression networks and clinical data","year":2019,"lang":"en","type":"article","venue":"Genomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Natural Science Foundation of Shaanxi Province; China Scholarship Council","keywords":"Gene isoform; Biology; Breast cancer; Computational biology; Biomarker; Drug; Cancer; Drug target; Gene expression; Bioinformatics; Genetics; Cancer research; Gene; Pharmacology","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":[0.001240856,0.0005500171,0.001004576,0.001833563,0.0002590542,0.001041885,0.0003545536,0.000423205,0.001179853],"category_scores_gemma":[0.00209442,0.0001665506,0.0006544215,0.00213978,0.0002939504,0.0005476267,0.0004369461,0.0006066054,0.0004954002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576119,"about_ca_system_score_gemma":0.0005893318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006675069,"about_ca_topic_score_gemma":0.001398888,"domain_scores_codex":[0.99941,0.0001334983,0.00005728913,0.0002109754,0.0001210186,0.00006720555],"domain_scores_gemma":[0.9986696,0.0005550966,0.0004264357,0.0001108511,0.0001505596,0.00008736573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003410283,0.000431945,0.687569,0.0005755576,0.001247027,0.0009065208,0.0001334633,0.01899448,0.1238175,0.001804952,0.004434974,0.1566744],"study_design_scores_gemma":[0.000161993,0.0007545924,0.8003908,0.0001646633,0.001923378,0.002996506,0.0002572295,0.1259392,0.04107906,0.01272388,0.01353258,0.00007611081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9079009,0.01066598,0.05842033,0.001270972,0.00009666359,0.0001469125,0.0161721,0.000657066,0.004669147],"genre_scores_gemma":[0.9724609,0.001734145,0.01534912,0.0002347662,0.0001152484,0.000096216,0.009208632,0.00005779083,0.0007432598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001833563,"threshold_uncertainty_score":0.006562293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928479653846859,"score_gpt":0.2896273579220047,"score_spread":0.2703425613835361,"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."}}