{"id":"W4389130353","doi":"10.1371/journal.pone.0294772","title":"Traditional Chinese Medicine studies for Alzheimer’s disease via network pharmacology based on entropy and random walk","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Active ingredient; Random walk; Entropy (arrow of time); Random forest; Traditional Chinese medicine; Computer science; Similarity (geometry); Artificial intelligence; Chinese herbs; Medicine; Machine learning; Computational biology; Mathematics; Pharmacology; Biology; Statistics; Alternative medicine","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":[0.0006511084,0.0006058418,0.0006146365,0.002190131,0.0005862128,0.0008077275,0.0005611912,0.0004875026,0.001867002],"category_scores_gemma":[0.002434132,0.0002267991,0.001308837,0.001161356,0.0004620104,0.001603715,0.0005965343,0.0004874281,0.0001417252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006727,"about_ca_system_score_gemma":0.0007274204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007001564,"about_ca_topic_score_gemma":0.006125048,"domain_scores_codex":[0.9996219,0.0001162245,0.00003083784,0.0001210648,0.0000779026,0.00003205194],"domain_scores_gemma":[0.9992963,0.0003850369,0.0001339368,0.00004035804,0.0001027217,0.00004174487],"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":[0.0002472255,0.0002106862,0.05628906,0.0005502798,0.0008825551,0.0007196617,0.0004120694,0.6941859,0.004598181,0.08891884,0.004828989,0.1481566],"study_design_scores_gemma":[0.00001398068,0.00005157059,0.005903791,0.00002691878,0.000126924,0.0001064683,0.00004911017,0.9613693,0.0005036712,0.03056285,0.001265404,0.00001992304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3048335,0.005536927,0.6749913,0.002429424,0.0001551203,0.0002338139,0.0009408029,0.0004692215,0.01040998],"genre_scores_gemma":[0.9491153,0.002079816,0.04489616,0.0001667254,0.0001042751,0.0001586174,0.0006114801,0.00002404974,0.002843655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007001564,"threshold_uncertainty_score":0.01392168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1601001731305063,"score_gpt":0.3603584687972785,"score_spread":0.2002582956667721,"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."}}