{"id":"W6939115016","doi":"10.60692/fjhkk-ct395","title":"Interpretable AI for drug response prediction","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Documentation; Code (set theory); Training set; Deep learning; Data modeling; Drug response","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":[0.002487907,0.001576829,0.000655881,0.001737734,0.0004108716,0.0015456,0.002002843,0.001376535,0.0409105],"category_scores_gemma":[0.01592705,0.0004440158,0.001828229,0.001734948,0.0002990729,0.001051449,0.001136579,0.00293747,0.02345972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001390552,"about_ca_system_score_gemma":0.001746256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007833929,"about_ca_topic_score_gemma":0.0147555,"domain_scores_codex":[0.9984162,0.0005894327,0.0001315774,0.0004069483,0.0003553499,0.0001004387],"domain_scores_gemma":[0.9949869,0.003133366,0.0002420968,0.0009865966,0.0005097758,0.0001413131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001187497,0.0004753188,0.009885909,0.001458489,0.0004189965,0.0001962882,0.0001052806,0.03127353,0.002116737,0.003935497,0.7934346,0.1555119],"study_design_scores_gemma":[0.0009254455,0.0007211381,0.01840434,0.0006980188,0.0003776408,0.0005080322,0.000178004,0.3398733,0.01191012,0.04010815,0.5861164,0.0001793117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0247598,0.002873263,0.07131023,0.005366805,0.001289872,0.0007005059,0.817066,0.05516522,0.02146828],"genre_scores_gemma":[0.08516507,0.001122365,0.08649807,0.001893385,0.0003314361,0.001182211,0.8123386,0.001819574,0.009649206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0409105,"threshold_uncertainty_score":0.1368593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019169613593481,"score_gpt":0.2392748330460499,"score_spread":0.2190831369101151,"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."}}