{"id":"W3042645205","doi":"10.1093/bioinformatics/btaa442","title":"AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Canada Foundation for Innovation","keywords":"Pharmacogenomics; Adaptation (eye); Adversarial system; Computer science; Transfer of learning; Space (punctuation); Artificial intelligence; Machine learning; Biology; Bioinformatics; Neuroscience","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":[0.002676699,0.001562924,0.001059697,0.0006998372,0.0003816017,0.000872336,0.002367276,0.001668431,0.002742462],"category_scores_gemma":[0.008658197,0.0004564858,0.0009652862,0.0007931477,0.001700743,0.001676874,0.002723363,0.003697586,0.0008435112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242216,"about_ca_system_score_gemma":0.001171593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003408111,"about_ca_topic_score_gemma":0.001891509,"domain_scores_codex":[0.9988151,0.0005475108,0.00005563952,0.00024757,0.0002310306,0.00010325],"domain_scores_gemma":[0.996051,0.002924812,0.0002455018,0.0002905441,0.0003472439,0.0001408794],"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":[0.0001270883,0.0001227867,0.001208947,0.0001026612,0.00008547179,0.0001143443,0.00006730758,0.9220305,0.001602684,0.006417927,0.003345818,0.06477435],"study_design_scores_gemma":[0.000004712339,0.00002768914,0.00005332479,0.000006302926,0.000004677054,0.00001089532,0.000003631006,0.9926347,0.0004403153,0.006583239,0.0002258541,0.000004714015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01441342,0.0009772173,0.9797505,0.0008130185,0.00009619995,0.00007666569,0.0002103153,0.001938773,0.001723828],"genre_scores_gemma":[0.8491237,0.0007539916,0.1402822,0.001572081,0.0002178124,0.00045895,0.001296685,0.0002891992,0.006005332],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003408111,"threshold_uncertainty_score":0.01415586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01805784941033034,"score_gpt":0.2380908859374603,"score_spread":0.22003303652713,"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."}}