{"id":"W2306570595","doi":"10.1021/acs.molpharmaceut.5b00982","title":"Applications of Deep Learning in Biomedicine","year":2016,"lang":"en","type":"review","venue":"Molecular Pharmaceutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":715,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biomedicine; Deep learning; Computer science; Artificial intelligence; Machine learning; Drug discovery; Data science; Identification (biology); Key (lock); Artificial neural network; Biomarker discovery; Deep neural networks; Bioinformatics; Biology; Proteomics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006938762,0.0002581323,0.0007793223,0.0005911821,0.00002751394,0.00002456984,0.001129565,0.0001203179,0.00001315661],"category_scores_gemma":[0.00007766402,0.0002124618,0.0002300915,0.001404946,0.00007316288,0.00008749199,0.0004519897,0.0003788216,0.0000482454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173885,"about_ca_system_score_gemma":0.0002379137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001469273,"about_ca_topic_score_gemma":2.972408e-7,"domain_scores_codex":[0.9977428,0.0005542218,0.0006520073,0.0004313213,0.0003712018,0.0002484513],"domain_scores_gemma":[0.9984433,0.000525962,0.0003772352,0.0004817815,0.00008161814,0.00009009626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[5.036939e-7,0.00003696137,7.276235e-7,0.002082174,0.00004200375,0.00002155573,0.00002830571,0.0002906546,0.00001899029,0.00633578,0.00000385967,0.9911385],"study_design_scores_gemma":[0.0001877045,0.0000173605,4.968901e-7,0.001754136,0.00007823011,0.00002384051,0.000001320006,0.0108308,0.00009117502,0.001327918,0.9854766,0.0002103862],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.383979e-7,0.5092935,0.4899303,0.00003795875,0.00007138713,0.0002976495,0.000003144688,0.00003008695,0.0003357802],"genre_scores_gemma":[0.00003298525,0.9719424,0.02769123,0.00006191633,0.00004751945,0.0001274986,0.00002037011,0.00002960689,0.00004647861],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9909281,"threshold_uncertainty_score":0.8663943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05247626487845024,"score_gpt":0.4336418701322031,"score_spread":0.3811656052537528,"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."}}