{"id":"W2579151866","doi":"10.1016/j.jbi.2017.03.006","title":"Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning","year":2017,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drug; Computer science; Drug repositioning; Drug discovery; Drug development; Heterogeneous network; Machine learning; Drug target; Algorithm; Data mining; Artificial intelligence; Bioinformatics; Medicine; Pharmacology; Biology","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":[0.003553281,0.001338506,0.00171278,0.002602808,0.001595112,0.00227857,0.003848197,0.002754051,0.004660548],"category_scores_gemma":[0.005528022,0.0009079319,0.001709982,0.00262527,0.001023655,0.002755156,0.003669891,0.002563597,0.001495057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092119,"about_ca_system_score_gemma":0.002456027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009328851,"about_ca_topic_score_gemma":0.0133563,"domain_scores_codex":[0.9987645,0.000322071,0.00008281966,0.0003108832,0.0004185539,0.0001012269],"domain_scores_gemma":[0.9974692,0.001302879,0.0001487895,0.0004052659,0.0005284126,0.0001455291],"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":[0.0004486134,0.0003767305,0.001570422,0.0002200856,0.0002519843,0.0001836336,0.0001751821,0.3463115,0.009666181,0.01230052,0.007978544,0.6205167],"study_design_scores_gemma":[0.00001824052,0.00002634811,0.00006726384,0.000004938021,0.00002010433,0.00002348857,0.00001003468,0.9933589,0.002470744,0.003210685,0.000778363,0.00001089244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007133628,0.0001649432,0.9887883,0.0001467047,0.00006575615,0.00007849532,0.0001674509,0.002788125,0.0006665224],"genre_scores_gemma":[0.07884379,0.0001623396,0.9163646,0.0002655888,0.00008413764,0.0001882899,0.0004664361,0.000741902,0.002882912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009328851,"threshold_uncertainty_score":0.01879179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857240276652383,"score_gpt":0.313755364473549,"score_spread":0.2951829617070252,"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."}}