{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001306452,0.0008310565,0.0007274245,0.001910267,0.0002453417,0.001174831,0.001091207,0.001212681,0.003500289],"category_scores_gemma":[0.00180635,0.0003404623,0.0006565521,0.002081142,0.0006901655,0.001341667,0.001314654,0.002223263,0.001629312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009423,"about_ca_system_score_gemma":0.001696915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001771937,"about_ca_topic_score_gemma":0.001993891,"domain_scores_codex":[0.9996438,0.00008210909,0.00003842867,0.00005548086,0.0001509214,0.00002931733],"domain_scores_gemma":[0.9991767,0.0005123349,0.00004787832,0.00003499327,0.0001868385,0.00004119552],"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":[0.00003471388,0.00006250138,0.0004449504,0.006858782,0.0001101731,0.000142366,0.00006537319,0.004116991,0.001318021,0.02219219,0.01572985,0.9489239],"study_design_scores_gemma":[0.00002037531,0.000107065,0.001118342,0.005669266,0.0001167761,0.001005273,0.00006546912,0.007022448,0.003558547,0.03846313,0.942795,0.00005824293],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008823144,0.9693158,0.01699205,0.003100095,0.0005136094,0.00004014413,0.0001130201,0.00009758193,0.00894551],"genre_scores_gemma":[0.009972091,0.9789367,0.007704497,0.000805161,0.0003370967,0.00003315926,0.0001274285,0.00001316622,0.002070805],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003500289,"threshold_uncertainty_score":0.01170963,"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."}}