{"id":"W4285231030","doi":"10.1007/978-1-0716-2124-0_8","title":"HUNTER: Sensitive Automated Characterization of Proteolytic Systems by N Termini Enrichment from Microscale Specimen","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; BC Cancer Agency; BC Children's Hospital; University of British Columbia","funders":"","keywords":"Microscale chemistry; Proteolysis; Computational biology; Rendering (computer graphics); Cell biology; Biology; Chemistry; Biological system; Biophysics; Computer science; Biochemistry; Enzyme; Mathematics; Artificial intelligence","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":[0.0009202487,0.0009856237,0.0009789626,0.0010337,0.0004323272,0.0009835473,0.001204119,0.0007863599,0.002460696],"category_scores_gemma":[0.001736633,0.0005870104,0.0005719316,0.0005461302,0.0005762274,0.0007613099,0.001227103,0.002153662,0.002434409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003473481,"about_ca_system_score_gemma":0.0005384322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005815504,"about_ca_topic_score_gemma":0.00203568,"domain_scores_codex":[0.9991665,0.00006937901,0.00004380673,0.0001900746,0.0004407553,0.00008958288],"domain_scores_gemma":[0.9989558,0.0004532003,0.0001469413,0.0002008525,0.0001405964,0.0001025472],"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":[0.0001503273,0.00005717437,0.0008607058,0.0002018874,0.00004331176,0.0001287352,0.00006030535,0.0002384909,0.9764481,0.0003638118,0.001836721,0.01961047],"study_design_scores_gemma":[0.0000263355,0.00008012255,0.003524922,0.00001705179,0.00002186532,0.0006713328,0.00003735337,0.01031687,0.9771596,0.0005024465,0.007604307,0.00003771685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3202841,0.00251248,0.6545795,0.0005910336,0.0001886565,0.0005839785,0.005660383,0.01277427,0.002825602],"genre_scores_gemma":[0.2743921,0.002270578,0.6935907,0.0006796086,0.00006501336,0.001193732,0.01408836,0.001904274,0.01181561],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002460696,"threshold_uncertainty_score":0.008231819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006253111814596853,"score_gpt":0.3235772253727734,"score_spread":0.3173241135581765,"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."}}