{"id":"W4285793128","doi":"10.3389/fdata.2022.972726","title":"Editorial: Big Data and machine learning in cancer theranostics","year":2022,"lang":"en","type":"editorial","venue":"Frontiers in Big Data","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Big data; Front (military); Data science; Computer science; Medicine; Engineering; Data mining","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":[0.007762674,0.004560123,0.004241124,0.004569693,0.004078977,0.008432859,0.004030357,0.01465685,0.03163352],"category_scores_gemma":[0.02689657,0.001256097,0.003263991,0.001616351,0.002410676,0.004991014,0.001735109,0.01898472,0.0235646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003144179,"about_ca_system_score_gemma":0.00354165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173365,"about_ca_topic_score_gemma":0.00507288,"domain_scores_codex":[0.9949813,0.0008258896,0.0005539866,0.0006106961,0.002641489,0.0003866296],"domain_scores_gemma":[0.9774268,0.008696273,0.001232162,0.0005185873,0.008489314,0.003636933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002709976,0.000006817425,0.000009552493,0.00007820829,0.000008545032,0.00004294325,0.000003913469,0.00001457155,0.00002131637,0.0001836697,0.9974834,0.002120037],"study_design_scores_gemma":[0.0001099615,0.00002988071,0.0001915928,0.0004064036,0.00004690045,0.0002035387,0.00002640312,0.0002147647,0.0001144295,0.001681264,0.9969532,0.00002164373],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00002093087,0.002890652,0.0001198042,0.03202485,0.962988,0.00002456463,0.0001052479,0.00007474017,0.001751163],"genre_scores_gemma":[0.0002875937,0.00227361,0.00009612517,0.01676074,0.9706684,0.00002578154,0.00005305955,0.0000476131,0.009786999],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03163352,"threshold_uncertainty_score":0.1058247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08452676457227666,"score_gpt":0.3373834505491007,"score_spread":0.252856685976824,"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."}}