{"id":"W2054353708","doi":"10.1021/ac8009017","title":"Sequential Interval Motif Search: Unrestricted Database Surveys of Global MS/MS Data Sets for Detection of Putative Post-Translational Modifications","year":2008,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Genomics Institute; Genome Canada","keywords":"Database search engine; Proteome; Chemistry; Computational biology; False positive paradox; Tandem mass spectrometry; Sequence database; Search engine; Data mining; Computer science; Mass spectrometry; Information retrieval; Artificial intelligence; Biochemistry; Biology; Chromatography; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002327273,0.000166559,0.0002576249,0.00002053989,0.0001080557,0.000008688703,0.0005357626,0.0001619634,0.0001427172],"category_scores_gemma":[0.0003085783,0.0001810988,0.0001241425,0.0003174479,0.0003444416,0.000160681,0.0001643165,0.0002128361,0.000001773697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007816381,"about_ca_system_score_gemma":0.0002170384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002531674,"about_ca_topic_score_gemma":0.00001365821,"domain_scores_codex":[0.9984842,0.00002686104,0.0005292024,0.0004581585,0.0002831212,0.0002184754],"domain_scores_gemma":[0.9981815,0.0002257997,0.0001944134,0.0007966437,0.0004803446,0.000121249],"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":[0.0001207887,0.0003725388,0.001999243,0.0003067174,0.0001426326,0.000002608241,0.00003640718,0.0003764997,0.9947741,0.0006666277,0.00008637586,0.001115447],"study_design_scores_gemma":[0.0004789134,0.0000200932,0.00135256,0.00002776839,0.00009160763,0.0000242207,0.00005017728,0.0713064,0.9252682,0.00108994,0.0001111151,0.0001790321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4996639,0.00002613343,0.4808019,0.0001440392,0.000006981046,0.0001727399,0.01811051,0.00005779097,0.001015998],"genre_scores_gemma":[0.9728373,0.00002175708,0.0185744,0.000006258874,0.00005809977,0.000044567,0.0083376,0.00001719441,0.0001027772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4731735,"threshold_uncertainty_score":0.7384996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1054292874879158,"score_gpt":0.3714334196807012,"score_spread":0.2660041321927854,"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."}}