{"id":"W2558818240","doi":"10.1038/srep38195","title":"A SILAC-Based Method for Quantitative Proteomic Analysis of Intestinal Organoids","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Stable isotope labeling by amino acids in cell culture; Organoid; Proteome; Quantitative proteomics; Biology; Proteomics; Cell biology; 3D cell culture; Crypt; Computational biology; Cell; Bioinformatics; Biochemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001033418,0.0001080669,0.0002683378,0.0003505185,0.0001160094,0.00004233507,0.0001590848,0.00005435849,0.002566324],"category_scores_gemma":[0.0005962631,0.00007503921,0.0002340019,0.001349329,0.0001687353,0.00004722259,0.00003315938,0.00004314323,0.000002263798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006798321,"about_ca_system_score_gemma":0.0001434594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002756327,"about_ca_topic_score_gemma":0.00001295402,"domain_scores_codex":[0.9984829,0.00001457461,0.0004831809,0.000579015,0.0002474195,0.0001929141],"domain_scores_gemma":[0.998031,0.0002229403,0.0005064957,0.0008259906,0.0003498988,0.00006370606],"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":[0.0000123984,0.00007947889,0.005407786,0.00004148293,0.00009482966,0.000003904187,0.00002427532,0.00001041772,0.9891447,0.003481054,0.0007697443,0.0009299246],"study_design_scores_gemma":[0.00008792702,0.00002722757,0.0001868986,0.00003795802,0.0002711388,0.000006739981,0.00001960616,0.003447363,0.9759762,0.01768818,0.002140043,0.0001106733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2770779,0.00001092869,0.7195541,0.00027724,0.00009528617,0.0003001697,0.00005166994,0.000118838,0.002513856],"genre_scores_gemma":[0.7209726,2.415037e-7,0.2759902,0.000004737775,0.00001001004,0.0002416628,0.00004784646,0.00001042477,0.0027223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4438947,"threshold_uncertainty_score":0.9983455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02416369380259078,"score_gpt":0.3350460355899589,"score_spread":0.3108823417873682,"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."}}