{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004495576,0.0001088804,0.0001021804,0.0000438699,0.00004189152,0.00008222056,0.00005914904,0.0000338747,0.000003440683],"category_scores_gemma":[0.000007947075,0.00008221369,0.00001136344,0.00004297975,0.00001786585,0.0001597066,0.00002862423,0.0001040395,0.00001127977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000244662,"about_ca_system_score_gemma":0.00001456894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002753592,"about_ca_topic_score_gemma":0.000001703683,"domain_scores_codex":[0.999552,0.000001418138,0.00008548967,0.0001250487,0.00004229054,0.0001938221],"domain_scores_gemma":[0.9998493,0.00003796006,0.00001024825,0.00007458092,0.00001034895,0.00001762306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005873641,0.00003194002,0.07279404,0.0004393022,0.0002278885,0.000004042566,0.0009090186,0.0002048103,0.6776373,0.001458922,0.006026468,0.2402075],"study_design_scores_gemma":[0.0004782778,0.00007206298,0.0002019478,0.00003471631,0.000004746215,0.00001611171,0.0003142321,0.0002296323,0.7740331,0.0000372147,0.2243413,0.0002367512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695069,0.003838603,0.02446359,0.00005974898,0.00003653265,0.0001618879,0.000003981912,0.000775483,0.001153246],"genre_scores_gemma":[0.9904618,0.0002337132,0.003646722,0.00001501433,0.000007256338,0.00001197006,0.00002105138,0.00002789245,0.005574617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2399708,"threshold_uncertainty_score":0.3352577,"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."}}