{"id":"W1999957644","doi":"10.1515/hsz-2012-0269","title":"CLIPPER: an add-on to the Trans-Proteomic Pipeline for the automated analysis of TAILS N-terminomics data","year":2012,"lang":"en","type":"article","venue":"Biological Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"European Commission; Canadian Breast Cancer Research Alliance; Canadian Institutes of Health Research; Cancer Research Society; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; FP7 People: Marie-Curie Actions; National Science Foundation","keywords":"Clipper (electronics); Pipeline (software); Computer science; Proteomics; Protease; Computational biology; Annotation; Chemistry; Biology; Biochemistry; Artificial intelligence; Operating system; Engineering","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":[0.005677666,0.002744706,0.001486528,0.002164442,0.001330027,0.003380848,0.003492271,0.001372126,0.01030364],"category_scores_gemma":[0.009693158,0.001735069,0.001789444,0.00161652,0.0009970127,0.00327815,0.004217824,0.005204194,0.01086282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006774718,"about_ca_system_score_gemma":0.001932902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294569,"about_ca_topic_score_gemma":0.002164108,"domain_scores_codex":[0.9978907,0.0003726958,0.0001900749,0.0005181759,0.0008060885,0.0002222923],"domain_scores_gemma":[0.9949508,0.001632893,0.0004232443,0.001452408,0.0009786146,0.0005621715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003482916,0.0006299368,0.006086018,0.001966746,0.0009756513,0.002164523,0.001108441,0.006013172,0.3620364,0.009199505,0.2088727,0.3974639],"study_design_scores_gemma":[0.0004399913,0.0007042828,0.009162535,0.0001830856,0.0002872394,0.003626809,0.0002216021,0.1642042,0.595423,0.01896407,0.2060583,0.0007249638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009605032,0.0003890416,0.8000196,0.0005883683,0.000271542,0.0004419911,0.007097669,0.1796262,0.00196048],"genre_scores_gemma":[0.03993344,0.0005885552,0.9195173,0.0007860761,0.0002200038,0.0009384666,0.01966031,0.01491699,0.003438917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01030364,"threshold_uncertainty_score":0.03446913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0536990715586675,"score_gpt":0.3395566865141707,"score_spread":0.2858576149555032,"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."}}