{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008404072,0.0002008632,0.0003457877,0.0001107959,0.00006159845,0.00001240023,0.0002835573,0.0001712453,0.00003062424],"category_scores_gemma":[0.0001427526,0.0002102816,0.00007693989,0.0001775559,0.00009853893,0.00000294237,0.0004320364,0.0002221155,0.000002283416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006013787,"about_ca_system_score_gemma":0.00004989669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009940234,"about_ca_topic_score_gemma":0.00000101544,"domain_scores_codex":[0.996962,0.001721281,0.0005521579,0.0003821154,0.0001127127,0.0002698108],"domain_scores_gemma":[0.9990745,0.00004392494,0.0003723686,0.0004168984,0.00004902836,0.00004324663],"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":[0.00006689438,0.0000815544,0.002359971,0.0000244754,0.00006575053,0.000004297002,0.000162034,0.0002625748,0.9957352,0.00005261324,0.00009441268,0.00109027],"study_design_scores_gemma":[0.0006408132,0.0005024982,0.001223322,0.00001380717,0.0000201916,0.0000196024,0.0001302133,0.01371283,0.9671925,0.00003224651,0.01625706,0.0002548834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6030525,0.0001554669,0.3952044,0.00005589546,0.0002953448,0.0005498119,0.0003692835,0.00003416264,0.0002832116],"genre_scores_gemma":[0.6794389,0.00004041849,0.3128648,0.0005806863,0.00007982852,0.0003213143,0.006146776,0.00006519026,0.0004620296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0823395,"threshold_uncertainty_score":0.8575034,"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."}}