{"id":"W4389858821","doi":"10.21203/rs.3.rs-3676579/v1","title":"Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Else Kröner-Fresenius-Stiftung; U.S. Department of Defense; Medizinischen Hochschule Hannover; Deutsche Forschungsgemeinschaft; Niedersächsisches Ministerium für Wissenschaft und Kultur","keywords":"In silico; Drug discovery; Generative grammar; Computational biology; Drug; Computer science; Dynamics (music); Drug repositioning; Artificial intelligence; Biology; Bioinformatics; Pharmacology; Psychology; Genetics","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":[0.000744958,0.001071676,0.001129225,0.0006787119,0.0004147506,0.001283661,0.001868914,0.002502502,0.006124499],"category_scores_gemma":[0.003275733,0.001063031,0.001359351,0.0005916686,0.0008004775,0.001340026,0.001605769,0.002512269,0.001838826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042817,"about_ca_system_score_gemma":0.001204378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00804361,"about_ca_topic_score_gemma":0.01279007,"domain_scores_codex":[0.9997967,0.00006975439,0.000007676171,0.00005662069,0.00004637122,0.00002300951],"domain_scores_gemma":[0.9991981,0.0005325578,0.00003196404,0.0001357738,0.00004994686,0.00005168275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001523692,0.00004544847,0.0007109329,0.0001025299,0.0001212557,0.0001245742,0.00005224172,0.9034923,0.002030959,0.03133561,0.01063218,0.05119956],"study_design_scores_gemma":[0.000009046171,0.00000422965,0.00003253491,0.000004017515,0.000005013578,0.0000120879,0.0000011573,0.9848963,0.0002851427,0.01393138,0.0008155438,0.000003466632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01126611,0.0007868111,0.9740356,0.0009018928,0.0001595779,0.00004264875,0.001988633,0.008192562,0.002626089],"genre_scores_gemma":[0.5466701,0.001320337,0.419011,0.001176162,0.0003945687,0.000500899,0.007425869,0.003812094,0.01968894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00804361,"threshold_uncertainty_score":0.0204885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0515602745414627,"score_gpt":0.3814993627509856,"score_spread":0.3299390882095229,"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."}}