{"id":"W2979442428","doi":"10.1109/ccece.2019.8861842","title":"Machine Learning with Digital Microfluidics for Drug Discovery and Development","year":2019,"lang":"en","type":"article","venue":"","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Drug discovery; Computer science; Context (archaeology); Machine learning; Biopharmaceutical; Microfluidics; Digital microfluidics; Artificial intelligence; Engineering; Nanotechnology; Bioinformatics; Biotechnology","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":[0.001131333,0.0006020233,0.0007144262,0.001019985,0.0002623531,0.001411208,0.0007056752,0.000904467,0.002490456],"category_scores_gemma":[0.002521591,0.0003459152,0.0006473101,0.0008540925,0.001019887,0.001892563,0.001001922,0.001362758,0.001131216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000984482,"about_ca_system_score_gemma":0.0009655816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005215856,"about_ca_topic_score_gemma":0.000607009,"domain_scores_codex":[0.9993082,0.0001979145,0.00005142133,0.0001142206,0.0002930856,0.00003513884],"domain_scores_gemma":[0.9994699,0.0002930358,0.00007005347,0.00006935601,0.00007326412,0.00002433242],"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":[0.0002055752,0.0001512922,0.001040921,0.001591389,0.0001499499,0.0001602228,0.0001147185,0.05194953,0.0493445,0.2635459,0.0136435,0.6181024],"study_design_scores_gemma":[0.00008932201,0.0005370307,0.000877362,0.0005158564,0.0001236781,0.0005493345,0.00005904128,0.4027242,0.07919819,0.2259103,0.2892431,0.0001726859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005011918,0.06937613,0.9050995,0.003581221,0.0009922704,0.0001495098,0.0002649004,0.001463724,0.01406082],"genre_scores_gemma":[0.1846282,0.06944753,0.7303339,0.001591736,0.001063182,0.0005767668,0.0004001042,0.0001413501,0.0118172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002490456,"threshold_uncertainty_score":0.008331418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003143845676193829,"score_gpt":0.15935988921475,"score_spread":0.1562160435385562,"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."}}