{"id":"W1974882000","doi":"10.1371/journal.pone.0091507","title":"The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"","keywords":"Computer science; Probabilistic logic; Algorithm; Inference; Tree (set theory); Theoretical computer science; Variable elimination; Mathematics; Combinatorics; 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":[0.002993441,0.000897427,0.0009470604,0.001240321,0.0008148212,0.001614292,0.002074867,0.001128636,0.008560176],"category_scores_gemma":[0.009132029,0.0008672758,0.00132608,0.001788899,0.0009192085,0.003290041,0.001850134,0.001984976,0.002597634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398343,"about_ca_system_score_gemma":0.002882904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006514418,"about_ca_topic_score_gemma":0.01262837,"domain_scores_codex":[0.998772,0.0003071761,0.00008416214,0.0002182321,0.0005105064,0.0001078825],"domain_scores_gemma":[0.9972584,0.001683861,0.0001607563,0.000471388,0.0003382305,0.00008739674],"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":[0.0003763938,0.0001150921,0.002394639,0.0002716258,0.0001683717,0.0002696537,0.0002144704,0.2776517,0.01367848,0.1515527,0.01014112,0.5431657],"study_design_scores_gemma":[0.00002301521,0.00001859653,0.0001847286,0.00001467311,0.00002192705,0.00007477117,0.00001468674,0.9068582,0.003419934,0.08647665,0.002875608,0.00001719716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002578368,0.00008381053,0.9945225,0.0001110998,0.00002143859,0.00002392407,0.0001413501,0.001800033,0.0007175292],"genre_scores_gemma":[0.08251228,0.0001985449,0.914325,0.0001935786,0.00005781001,0.0001296401,0.0005278367,0.0004339885,0.001621324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008560176,"threshold_uncertainty_score":0.02863663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223566692612406,"score_gpt":0.2406124247992314,"score_spread":0.2183767578731073,"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."}}