{"id":"W4287169408","doi":"10.5281/zenodo.4793442","title":"Velodrome: Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Generalization; Distribution (mathematics); Drug response; Computational biology; Drug; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Biology; 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":[0.007498743,0.002792136,0.002348752,0.002335778,0.00100379,0.001702214,0.003827821,0.002903335,0.0105828],"category_scores_gemma":[0.01188889,0.001342586,0.00312072,0.001573634,0.001276757,0.002183449,0.00291589,0.003978028,0.005191341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337448,"about_ca_system_score_gemma":0.002135691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005423532,"about_ca_topic_score_gemma":0.01387187,"domain_scores_codex":[0.9966797,0.001106042,0.0002098342,0.001021284,0.0007842983,0.0001989398],"domain_scores_gemma":[0.9946166,0.003102859,0.0002643157,0.00135566,0.000442092,0.0002185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002199983,0.0007518133,0.006133437,0.0009414051,0.001122742,0.000815988,0.0001871958,0.1071272,0.02361324,0.02897315,0.285438,0.5426959],"study_design_scores_gemma":[0.0003131242,0.0002029498,0.003055589,0.000071399,0.0001104547,0.0003123271,0.00004125528,0.9021568,0.01393557,0.05633815,0.02336112,0.0001012542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01322942,0.0008493099,0.885312,0.001735605,0.0004934493,0.0003557759,0.01418364,0.08034627,0.003494581],"genre_scores_gemma":[0.1862028,0.0006306839,0.746496,0.002086932,0.0006163018,0.0009776102,0.0428069,0.01148655,0.008696281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0105828,"threshold_uncertainty_score":0.03965759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04071786161767033,"score_gpt":0.2711840935567888,"score_spread":0.2304662319391185,"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."}}