{"id":"W2618922802","doi":"10.1021/acs.jproteome.7b00205","title":"CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization","year":2017,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Canadian Cancer Society Research Institute; Genome Canada; Canadian Institutes of Health Research; National Institutes of Health; Canada Research Chairs; Ministère de l'Enseignement Supérieur, de la Recherche, de la Science et de la Technologie","keywords":"Computer science; Visualization; Software suite; Software; Data mining; Workflow; Leverage (statistics); Suite; Mass spectrometry; Bottleneck; Tandem mass spectrometry; Computational science; Chemistry; Database; Programming language; Artificial intelligence; Embedded system","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":[0.00329078,0.003798322,0.002553312,0.004107434,0.001090128,0.003060621,0.004651069,0.002115465,0.04257882],"category_scores_gemma":[0.005994639,0.002225087,0.002500927,0.001986466,0.0007098689,0.002858505,0.003578417,0.004224384,0.0218109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442299,"about_ca_system_score_gemma":0.003223313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004541097,"about_ca_topic_score_gemma":0.004995733,"domain_scores_codex":[0.9983658,0.000277333,0.0002148651,0.0003414415,0.000588111,0.0002124639],"domain_scores_gemma":[0.9979035,0.001054105,0.0002279136,0.0002665144,0.0003871694,0.0001607768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001775122,0.0002301104,0.00440106,0.004688095,0.001200084,0.001431641,0.0009103547,0.009245055,0.05267007,0.0135635,0.7465891,0.1632958],"study_design_scores_gemma":[0.00121821,0.000270706,0.008249567,0.0009205625,0.0003029729,0.002121054,0.0002843527,0.2326623,0.09894368,0.03886992,0.6153502,0.0008064921],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.00608998,0.001336233,0.2523212,0.0004834095,0.000282338,0.000442292,0.03968028,0.6947121,0.004652192],"genre_scores_gemma":[0.04795865,0.002249112,0.5879794,0.00223001,0.0001690427,0.004545052,0.1341361,0.2107463,0.009986249],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04257882,"threshold_uncertainty_score":0.1424403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254342130458205,"score_gpt":0.4911894666267568,"score_spread":0.3657552535809363,"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."}}