{"id":"W3212105246","doi":"10.1101/2021.11.15.468703","title":"Sensitive identification of known and unknown protease activities by unsupervised linear motif deconvolution","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Protease; Proteases; Deconvolution; Peptide; Cleavage (geology); Computational biology; Biology; Biochemistry; Chemistry; Enzyme; Computer science; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001466311,0.000547533,0.0004085162,0.001362099,0.0002041492,0.0007304442,0.0005057913,0.0005241323,0.001264179],"category_scores_gemma":[0.001951856,0.0002256324,0.0004278471,0.0005730557,0.0003563065,0.0005541682,0.0005827076,0.0005645003,0.0007690056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002343406,"about_ca_system_score_gemma":0.0004109839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004359708,"about_ca_topic_score_gemma":0.0005663526,"domain_scores_codex":[0.9994614,0.0001259215,0.00004319678,0.0001689589,0.000147172,0.00005336461],"domain_scores_gemma":[0.9992234,0.0003222402,0.0001748925,0.0001177328,0.0001293731,0.00003242014],"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":[0.0007617968,0.0002077137,0.0144871,0.0001750724,0.0001152202,0.00014496,0.0000874442,0.007149417,0.8157307,0.001077165,0.00091685,0.1591465],"study_design_scores_gemma":[0.00003446194,0.0001409338,0.01937526,0.0000131168,0.00003208085,0.0006374186,0.00006942641,0.4797891,0.4958187,0.001855974,0.002190401,0.00004304638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3308324,0.0002944618,0.6631582,0.00008922235,0.0000179165,0.00009481136,0.0006305871,0.003867866,0.001014535],"genre_scores_gemma":[0.4861415,0.0001196529,0.5109965,0.00005525678,0.00001480253,0.00009812893,0.001048069,0.0002108913,0.001315302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001466311,"threshold_uncertainty_score":0.007754624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009349041580180918,"score_gpt":0.2301522477561657,"score_spread":0.2208032061759848,"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."}}