{"id":"W3089317162","doi":"10.1101/2020.09.23.309088","title":"Mapping Isoform Abundance and Interactome of the Endogenous TMPRSS2-ERG Fusion Protein with Orthogonal Immunoprecipitation-Mass Spectrometry Assays","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Mitacs; National Natural Science Foundation of China; Prostate Cancer Canada","keywords":"Interactome; TMPRSS2; Erg; Fusion protein; Gene isoform; Biology; Immunoprecipitation; Prostate cancer; Chromatin immunoprecipitation; Fusion gene; Molecular biology; Computational biology; Gene; Genetics; Gene expression; Cancer; Biochemistry; Internal medicine; Medicine; Recombinant DNA","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003789315,0.0004949362,0.0002661059,0.0009493157,0.0003051193,0.0005079947,0.0002804309,0.0002352769,0.001088143],"category_scores_gemma":[0.0002321632,0.0002025282,0.0003324128,0.0005661575,0.0002881635,0.000196846,0.0003053242,0.0004377607,0.0004914213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004092115,"about_ca_system_score_gemma":0.0002741354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009221599,"about_ca_topic_score_gemma":0.001261157,"domain_scores_codex":[0.9996232,0.00004230071,0.00003174486,0.0001321914,0.0001139872,0.00005655086],"domain_scores_gemma":[0.9998209,0.00004086838,0.00005404296,0.00001935832,0.00004276349,0.00002213997],"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":[0.00006022647,0.00001375423,0.001178218,0.00003308842,0.00001398844,0.00003358539,0.00001527699,0.00005805682,0.9974048,0.00008705326,0.00006612074,0.001035773],"study_design_scores_gemma":[0.000009896359,0.00008025448,0.02650207,0.000006777005,0.00005109889,0.0004607619,0.00006298453,0.005436653,0.9641935,0.0001298179,0.003052843,0.00001319815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9549442,0.001502294,0.03772601,0.0001606052,0.00003595954,0.00008857244,0.002963305,0.0004299431,0.002149124],"genre_scores_gemma":[0.9462385,0.00134226,0.04326047,0.0001692839,0.0000315579,0.0002033497,0.005172872,0.0001354975,0.003446179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001088143,"threshold_uncertainty_score":0.003640175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150974055857703,"score_gpt":0.2377374155507672,"score_spread":0.2162276749921902,"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."}}