{"id":"W2734233869","doi":"10.1101/160937","title":"Gene isoforms as expression-based biomarkers predictive of drug response <i>in vitro</i>","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Institute of Cancer Research; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Ontario Institute for Cancer Research; Canadian Cancer Society Research Institute; Government of Ontario; Cancer Research Society","keywords":"Pharmacogenomics; Erlotinib; Biomarker; Personalized medicine; Medicine; Computational biology; Drug; Breast cancer; Precision medicine; Cancer; Oncology; Biology; Bioinformatics; Internal medicine; Pharmacology; Genetics; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008339299,0.0009836677,0.00147389,0.003011881,0.0001957245,0.001998364,0.0006220277,0.0005377721,0.0007916763],"category_scores_gemma":[0.008493142,0.0002572916,0.003351807,0.00298512,0.0004452415,0.0005126419,0.0005403931,0.0007094776,0.0002134385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000917633,"about_ca_system_score_gemma":0.0007791118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0031489,"about_ca_topic_score_gemma":0.002396894,"domain_scores_codex":[0.9973199,0.001370705,0.000203086,0.0006305251,0.0003834623,0.00009245566],"domain_scores_gemma":[0.9926824,0.004837167,0.001251191,0.0007444542,0.0004034955,0.00008122801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002069258,0.0002090767,0.720614,0.002718306,0.0332554,0.0002578084,0.0001159096,0.1276623,0.03926764,0.00176196,0.002546249,0.06952202],"study_design_scores_gemma":[0.0001963296,0.002154417,0.5473796,0.0006041442,0.03318667,0.0008876077,0.0003750031,0.3237746,0.05813346,0.01289025,0.02019671,0.0002212283],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7700824,0.05339304,0.131108,0.002207873,0.0002039901,0.0003283063,0.03808168,0.0009445666,0.003650071],"genre_scores_gemma":[0.967632,0.004041969,0.01765433,0.0006150645,0.00008257459,0.0001766541,0.009308877,0.00008150204,0.0004069898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008339299,"threshold_uncertainty_score":0.04410297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006813448356631379,"score_gpt":0.2384188082956333,"score_spread":0.2316053599390019,"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."}}