{"id":"W4226144949","doi":"10.1186/s12859-022-04662-6","title":"Computationally repurposing drugs for breast cancer subtypes using a network-based approach","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Windsor Clinical Research; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Drug repositioning; Repurposing; Drug discovery; Computational biology; Drug; In silico; Disease; Drug development; DNA microarray; Interaction network; Bioinformatics; Computer science; Biology; Medicine; Pharmacology; Gene; 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":[0.0005029631,0.0008879051,0.0009390174,0.001367548,0.0004624559,0.0009331902,0.0009834985,0.001422628,0.005778885],"category_scores_gemma":[0.002327479,0.0005993011,0.001402026,0.001058117,0.0003170614,0.0007342558,0.0006635478,0.0008959271,0.000487249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189796,"about_ca_system_score_gemma":0.001605161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01729351,"about_ca_topic_score_gemma":0.02389362,"domain_scores_codex":[0.9998032,0.00005875701,0.0000133213,0.00004833662,0.00004086758,0.0000354254],"domain_scores_gemma":[0.9988689,0.000906008,0.0000725114,0.00003563512,0.00007074384,0.00004623682],"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":[0.00007472495,0.00004550123,0.001242222,0.00006408556,0.00005728618,0.0001140728,0.00001202381,0.9804648,0.0005487659,0.001623897,0.0008288714,0.01492376],"study_design_scores_gemma":[0.000008074589,0.000007840886,0.00009750859,0.000002278555,0.00001208033,0.00001368979,0.000003964101,0.9982612,0.0001171929,0.001248794,0.0002257684,0.000001671479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2242451,0.001718916,0.7515003,0.002943049,0.000152054,0.0004224736,0.003092496,0.002831612,0.01309391],"genre_scores_gemma":[0.7719117,0.000807438,0.2161526,0.0005892882,0.0001303588,0.0005358307,0.003671482,0.0001711636,0.006030011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01729351,"threshold_uncertainty_score":0.03438568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04285852233238176,"score_gpt":0.3053734704557206,"score_spread":0.2625149481233388,"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."}}