{"id":"W4405975703","doi":"10.1101/2024.12.30.630746","title":"Graph Network-Based Analysis of Disease-Gene-Drug Associations: Zero-Shot Disease-Drug Prediction and Analysis Strategies","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Drug repositioning; Workflow; Drug; Disease; Robustness (evolution); Machine learning; Graph; Artificial intelligence; Data mining; Computational biology; Medicine; Gene; Theoretical computer science; Biology; Pharmacology","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":[0.001652568,0.001177428,0.001022342,0.004004496,0.0006824482,0.0009122582,0.001801523,0.001050484,0.002217694],"category_scores_gemma":[0.004681421,0.0005176367,0.001543229,0.001946545,0.0008075736,0.001822932,0.001432756,0.001256176,0.0005120951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088672,"about_ca_system_score_gemma":0.001680163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007634721,"about_ca_topic_score_gemma":0.01102559,"domain_scores_codex":[0.9991578,0.0002567051,0.0000410577,0.0002587279,0.0002285065,0.00005723448],"domain_scores_gemma":[0.9974475,0.001476182,0.0002906015,0.000349778,0.0003129552,0.0001229566],"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":[0.0004884761,0.0003994266,0.0152772,0.0006162491,0.0005203017,0.0009512217,0.0002574715,0.5638825,0.01514579,0.02454106,0.009310478,0.3686099],"study_design_scores_gemma":[0.00001238368,0.00004008246,0.0006645544,0.00001124824,0.00004752316,0.0001121782,0.00002567781,0.9825343,0.002281271,0.01316297,0.001097436,0.00001027521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06624497,0.001162484,0.9236536,0.0006695385,0.00006740973,0.0001868338,0.001001387,0.004985227,0.002028502],"genre_scores_gemma":[0.6456562,0.0007099644,0.3470063,0.0003125977,0.00006225234,0.000115099,0.003544881,0.0002750786,0.002317507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007634721,"threshold_uncertainty_score":0.01518059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771705941416602,"score_gpt":0.2576732300562563,"score_spread":0.2399561706420902,"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."}}