{"id":"W2989451149","doi":"10.1371/journal.pone.0221796","title":"Protocol development for discovery of angiogenesis inhibitors via automated methods using zebrafish","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto; St. Michael's Hospital","funders":"Québec Consortium for Drug Discovery; Ontario Centres of Excellence","keywords":"Zebrafish; Angiogenesis; Drug discovery; Computational biology; Biology; Limiting; Bioinformatics; Cancer research; Biochemistry; Gene; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003118825,0.00234304,0.001480213,0.002202587,0.001592432,0.0007521685,0.002370768,0.001164543,0.0174174],"category_scores_gemma":[0.001947268,0.001893251,0.001290683,0.001428346,0.0008229971,0.0007880657,0.001252112,0.002456163,0.01151155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008099593,"about_ca_system_score_gemma":0.002153033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376387,"about_ca_topic_score_gemma":0.003840591,"domain_scores_codex":[0.9975432,0.00039508,0.0003841339,0.0003916048,0.001020177,0.0002658781],"domain_scores_gemma":[0.9987318,0.0003157809,0.0001620301,0.000322986,0.0003567646,0.0001105818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002774197,0.0001479927,0.0002695118,0.0008916484,0.00005995102,0.0003668595,0.0001766212,0.001137451,0.9609429,0.002587958,0.005873361,0.02726837],"study_design_scores_gemma":[0.0002238701,0.0007388428,0.001170567,0.0001245733,0.00009210234,0.0006116174,0.0000532579,0.006451622,0.8905728,0.001004034,0.09879147,0.0001651647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.02198603,0.001101378,0.939697,0.0002896571,0.0003176965,0.01332874,0.00594979,0.009467667,0.007862139],"genre_scores_gemma":[0.03018765,0.002856265,0.8982041,0.0003346778,0.00006904037,0.04503445,0.009699299,0.00111027,0.01250421],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.0174174,"threshold_uncertainty_score":0.058267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0524473570570807,"score_gpt":0.3620863164691182,"score_spread":0.3096389594120375,"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."}}