{"id":"W2904397976","doi":"10.1007/978-1-4939-8955-3_18","title":"Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning","year":2018,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Task (project management); Drug; Heterogeneous network; Machine learning; Data mining; Drug target; Artificial intelligence; Engineering; Medicine; 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":[0.002027152,0.001417289,0.001487576,0.001740782,0.001031179,0.001586921,0.004126621,0.002396559,0.007984957],"category_scores_gemma":[0.003836903,0.000964764,0.001985732,0.00152637,0.0009646464,0.001886834,0.003075592,0.003083422,0.002339821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107465,"about_ca_system_score_gemma":0.00231457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007219864,"about_ca_topic_score_gemma":0.01307753,"domain_scores_codex":[0.9992241,0.0002034493,0.00004414675,0.0001545669,0.0003106104,0.00006309483],"domain_scores_gemma":[0.9983467,0.0008429487,0.00009899597,0.0003322573,0.0002747023,0.0001043562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003900222,0.0002519978,0.0007475808,0.0003320927,0.0002902795,0.0001760004,0.0001267083,0.3996876,0.01271157,0.02447493,0.01562873,0.5451825],"study_design_scores_gemma":[0.000031244,0.00002438088,0.00003974682,0.000008134766,0.00002199342,0.00002139349,0.000005700747,0.9883692,0.002622659,0.006889697,0.00195334,0.00001234317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001939539,0.00009762611,0.9944347,0.00009727695,0.00005308721,0.0000600319,0.0001766987,0.002437141,0.0007039351],"genre_scores_gemma":[0.05130896,0.0001610332,0.941372,0.0003054271,0.00009785283,0.0002942203,0.0006496109,0.00113722,0.004673672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007984957,"threshold_uncertainty_score":0.02671236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02880713769146019,"score_gpt":0.4295152393717107,"score_spread":0.4007081016802505,"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."}}