{"id":"W1561803476","doi":"10.1007/11415770_26","title":"A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Canadian Institute for Theoretical Astrophysics","keywords":"Hidden Markov model; Computer science; Tandem mass spectrometry; Database search engine; Mascot; Function (biology); Markov chain; Identification (biology); Pattern recognition (psychology); Mass spectrometry; Artificial intelligence; Search engine; Machine learning; Chemistry; Information retrieval; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003945838,0.001229527,0.002186655,0.001758353,0.000958611,0.001501606,0.003344757,0.001983901,0.005816231],"category_scores_gemma":[0.008275702,0.0007863651,0.00156583,0.002615156,0.0004933858,0.002168747,0.001877204,0.001695713,0.004892683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009238938,"about_ca_system_score_gemma":0.001835686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004595198,"about_ca_topic_score_gemma":0.006416923,"domain_scores_codex":[0.9981224,0.0006396355,0.0001599433,0.0002227431,0.0007385013,0.0001167226],"domain_scores_gemma":[0.9963373,0.002076611,0.0001710396,0.0004915498,0.0008119441,0.0001116219],"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":[0.0008353463,0.0005853106,0.002193762,0.0003872958,0.0003813288,0.0002618169,0.00009468725,0.1315031,0.01850886,0.01541536,0.03255476,0.7972783],"study_design_scores_gemma":[0.00004528907,0.00006593639,0.0005077319,0.00001618956,0.00004834366,0.0001298351,0.00001461161,0.9745883,0.005145472,0.01720594,0.002193381,0.00003903644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005389846,0.0002750681,0.9865626,0.0001119841,0.00006247673,0.00009719621,0.0005217568,0.006420149,0.0005590832],"genre_scores_gemma":[0.07822645,0.0003068931,0.9130375,0.0002074783,0.00006736239,0.0003215608,0.003033504,0.0008366401,0.003962507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005816231,"threshold_uncertainty_score":0.02086788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523509746304136,"score_gpt":0.2860759256920249,"score_spread":0.2608408282289835,"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."}}