{"id":"W3002715866","doi":"10.1016/j.jcyt.2019.12.006","title":"Predicting in vitro human mesenchymal stromal cell expansion based on individual donor characteristics using machine learning","year":2020,"lang":"en","type":"article","venue":"Cytotherapy","topic":"Mesenchymal stem cell research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"European Research Council; KU Leuven; Fonds Wetenschappelijk Onderzoek; European Commission; Seventh Framework Programme; Fonds De La Recherche Scientifique - FNRS; European Resuscitation Council; Fiducie de Recherche sur la Foret des Cantons-de-l'Est; Environmental Restoration and Conservation Agency; Agentschap Innoveren en Ondernemen","keywords":"Mesenchymal stem cell; In vitro; Stromal cell; Cell biology; Computer science; Chemistry; Biology; Cancer research; Biochemistry","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":[0.0006498542,0.0003836131,0.0005244896,0.0006911004,0.0001000542,0.0005811127,0.0002421915,0.0003497509,0.0006349501],"category_scores_gemma":[0.00130788,0.0001295543,0.0004038847,0.0004548346,0.000158848,0.0003481449,0.0002316433,0.0003846217,0.0003075747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002878329,"about_ca_system_score_gemma":0.0002805456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008399716,"about_ca_topic_score_gemma":0.001332216,"domain_scores_codex":[0.9998098,0.00004259374,0.00001833839,0.00004973339,0.00005644003,0.00002303728],"domain_scores_gemma":[0.9989548,0.000690673,0.0001135504,0.00005431284,0.0001336438,0.00005301276],"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":[0.001481026,0.0008478966,0.2657024,0.0001901599,0.0002491367,0.0003318921,0.00007905134,0.3066491,0.1197564,0.0004024344,0.001449023,0.3028614],"study_design_scores_gemma":[0.00001459456,0.0002693709,0.02740087,0.00001081882,0.00007168227,0.0001551249,0.00003701172,0.9416476,0.02957947,0.0003891829,0.0004079753,0.00001623763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937969,0.0006467412,0.059659,0.00007697265,0.00003176272,0.00004720602,0.0004446874,0.0002589607,0.0008656384],"genre_scores_gemma":[0.9912333,0.0001645372,0.007902912,0.00002646303,0.00001169296,0.00002572494,0.0003346918,0.00001090961,0.0002898818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008399716,"threshold_uncertainty_score":0.003436804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0665934146458047,"score_gpt":0.3176837212750055,"score_spread":0.2510903066292008,"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."}}