{"id":"W2921358490","doi":"10.1371/journal.pcbi.1006273","title":"Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines","year":2019,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Seventh Framework Programme; Institut National Du Cancer; Bundesministerium für Bildung und Forschung; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; CNIB","keywords":"Cell growth; Spheroid; Biological system; Cell; Growth curve (statistics); Cell culture; Stress (linguistics); Biophysics; Materials science; Chemistry; Biology; Mathematics; 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":[0.000164404,0.0004318692,0.0004646011,0.000284589,0.0002311527,0.0005152443,0.0008480816,0.001063196,0.001245955],"category_scores_gemma":[0.0007301254,0.0002447977,0.0005111303,0.0002983254,0.0003855812,0.0004363616,0.0002953778,0.0004075205,0.0002562093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009705195,"about_ca_system_score_gemma":0.0007617108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01064749,"about_ca_topic_score_gemma":0.005546473,"domain_scores_codex":[0.9999247,0.00001208999,0.000004554322,0.00002397636,0.0000208655,0.00001382613],"domain_scores_gemma":[0.9997576,0.0001072215,0.00003636391,0.00003053933,0.0000454951,0.00002282745],"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":[0.000009329642,0.00001221221,0.0005845659,0.00001647154,0.000006491684,0.00001691079,0.00001207704,0.9910318,0.006653859,0.0009884364,0.00007011279,0.0005978353],"study_design_scores_gemma":[0.000002641164,0.000005118814,0.0002921281,7.554312e-7,0.000002353111,0.000005677531,0.00000271037,0.9986247,0.0006196194,0.0003594537,0.00008214884,0.000002755358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7112669,0.0003100228,0.2747265,0.0005385971,0.00005891875,0.00007950729,0.00183597,0.0006535444,0.01053003],"genre_scores_gemma":[0.9839801,0.0001780979,0.0125569,0.00006936288,0.000008654618,0.0001577361,0.0005110493,0.00008440636,0.002453687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01064749,"threshold_uncertainty_score":0.02117097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122762991535503,"score_gpt":0.2551213047584993,"score_spread":0.2438936748431443,"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."}}