{"id":"W2029719622","doi":"10.1016/j.chembiol.2012.04.007","title":"Dissecting Heterogeneous Molecular Chaperone Complexes Using a Mass Spectrum Deconvolution Approach","year":2012,"lang":"en","type":"article","venue":"Chemistry & Biology","topic":"Heat shock proteins research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute of General Medical Sciences; Wellcome Trust","keywords":"Heat shock protein; Chaperone (clinical); Mass spectrometry; Biophysics; Chemistry; Molecular mass; Biology; Computational biology; Biochemistry; Chromatography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003256949,0.0002731287,0.0002544083,0.00003405213,0.0001666687,0.00002069,0.0003146445,0.0004085632,0.0001018158],"category_scores_gemma":[0.000171323,0.0002762395,0.0001310072,0.000123137,0.00022443,0.000006675559,0.0002556996,0.0002274552,0.00001496385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008956434,"about_ca_system_score_gemma":0.00006905186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003454866,"about_ca_topic_score_gemma":0.000001158027,"domain_scores_codex":[0.9980268,0.00009727773,0.000279008,0.00054202,0.0001176647,0.000937254],"domain_scores_gemma":[0.9990379,0.00001242622,0.0000869354,0.0005268388,0.00007834991,0.0002575233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004372545,0.00009203053,0.005564528,0.00006365465,0.00007033322,0.000003091572,0.00002120752,0.000165483,0.9937347,0.00004192492,0.00001168326,0.0001876517],"study_design_scores_gemma":[0.0003056925,0.00007115531,0.0001367219,0.000007533069,0.0000146359,0.0002257813,0.00005595529,0.001251002,0.9954087,0.00008588724,0.002130903,0.0003060648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678885,0.002762296,0.02720125,0.00003628485,0.0001056845,0.0002339938,0.00002185649,0.00003419795,0.001715968],"genre_scores_gemma":[0.9952738,0.00003883875,0.003169835,0.00005173133,0.0007967298,0.00004806089,0.0003778216,0.00004693356,0.0001962214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02738536,"threshold_uncertainty_score":0.999969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740449281910972,"score_gpt":0.3050646105749245,"score_spread":0.2776601177558148,"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."}}