{"id":"W2995072917","doi":"10.1101/872069","title":"A multi-dimensional, time-lapse, high content screening platform applied to schistosomiasis drug discovery","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Parasites and Host Interactions","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"","keywords":"Drug discovery; Mahalanobis distance; Schistosoma mansoni; Population; Schistosomiasis; Biology; Computational biology; Phenotypic screening; Computer science; Phenotype; Bioinformatics; Immunology; Medicine; Artificial intelligence; Genetics; Helminths; Environmental health","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":[0.0004522596,0.0004028418,0.0005818335,0.0005831756,0.0002583429,0.000716358,0.0004240238,0.0004477877,0.002518789],"category_scores_gemma":[0.0002942703,0.0002865518,0.0003466369,0.0003309453,0.0002382471,0.0003268711,0.0005958689,0.000601556,0.0007967121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003089319,"about_ca_system_score_gemma":0.0002757698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005239332,"about_ca_topic_score_gemma":0.0009143937,"domain_scores_codex":[0.9997804,0.00003066315,0.00001146046,0.00004433748,0.0001102128,0.00002294152],"domain_scores_gemma":[0.9998011,0.00006173862,0.00004051868,0.00003529707,0.00003402313,0.00002743666],"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":[0.00007839881,0.00004379586,0.0003322052,0.00003726422,0.00001128573,0.00003460505,0.000008132693,0.001047557,0.9916214,0.0001959671,0.0002849252,0.006304634],"study_design_scores_gemma":[0.00004823279,0.0003091496,0.006687105,0.00001162919,0.00002892609,0.00020727,0.00002014433,0.06267251,0.9259016,0.0003873901,0.003691942,0.00003417068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6822086,0.001004224,0.3015844,0.000508559,0.0001370395,0.0004933073,0.005373762,0.004910985,0.003779221],"genre_scores_gemma":[0.6106267,0.0009353285,0.3791815,0.0002858012,0.00005910195,0.0007836799,0.003231215,0.0003176773,0.004578872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002518789,"threshold_uncertainty_score":0.008426249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02430156078545251,"score_gpt":0.2370036462109017,"score_spread":0.2127020854254492,"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."}}