{"id":"W3046562558","doi":"10.1038/s41598-020-70026-w","title":"Synergistic drug combinations and machine learning for drug repurposing in chordoma","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Multiple Myeloma Research and Treatments","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Ministero dello Sviluppo Economico; Ontario Ministry of Economic Development and Innovation; UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill; Novartis Pharma; Wellcome Trust; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Pfizer; Chordoma Foundation; Research Opportunities Initiative, University of North Carolina; Genome Canada","keywords":"Chordoma; Medicine; Drug repositioning; Repurposing; Palbociclib; PI3K/AKT/mTOR pathway; Drug; Pharmacology; Oncology; Cancer research; Cancer; Internal medicine; Biology; Signal transduction; Surgery","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001895353,0.0008648839,0.001367164,0.001342101,0.0002134461,0.0006947168,0.0005482132,0.0005188814,0.002027918],"category_scores_gemma":[0.002319825,0.000390044,0.00128544,0.0007037276,0.0003801765,0.0007059678,0.0005371384,0.001017334,0.0003042122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148373,"about_ca_system_score_gemma":0.0007929232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001675066,"about_ca_topic_score_gemma":0.00226611,"domain_scores_codex":[0.9992144,0.000387945,0.0000596476,0.0001121807,0.0001675427,0.00005822498],"domain_scores_gemma":[0.998952,0.0006796488,0.0001811993,0.00005678215,0.00009006567,0.00004023387],"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":[0.0008593234,0.0007640299,0.005539531,0.0003517891,0.0004181794,0.000174447,0.00002086105,0.842823,0.01827942,0.002980841,0.001450896,0.1263377],"study_design_scores_gemma":[0.0000729028,0.001041446,0.0009941453,0.00001749807,0.000118113,0.0001071403,0.000007470873,0.985121,0.008994941,0.002331231,0.00116837,0.00002567891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6520299,0.01159526,0.3161106,0.002094081,0.0001708032,0.0006743352,0.001508087,0.002217779,0.01359924],"genre_scores_gemma":[0.9417483,0.001239723,0.05486716,0.0002319356,0.00004325459,0.0001574686,0.0004328395,0.00003239081,0.001246964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002027918,"threshold_uncertainty_score":0.01002371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484766422189033,"score_gpt":0.3028092606433167,"score_spread":0.2679615964214264,"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."}}