{"id":"W2000237365","doi":"10.1016/j.dib.2015.02.007","title":"Dataset from proteomic analysis of rat, mouse, and human liver microsomes and S9 fractions","year":2015,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proteomics; Chromatography; Tandem mass spectrometry; Chemistry; Mass spectrometry; Shotgun proteomics; Trypsin; Bottom-up proteomics; Quantitative proteomics; Computational biology; Biochemistry; Biology; Protein mass spectrometry; Enzyme","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":[0.001427541,0.00277255,0.002664447,0.005109374,0.001199215,0.001920803,0.002495115,0.002531275,0.01424051],"category_scores_gemma":[0.003252944,0.0004655842,0.002001276,0.007432741,0.0004164199,0.0008566566,0.002271224,0.001334876,0.02082177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252305,"about_ca_system_score_gemma":0.002240403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005117095,"about_ca_topic_score_gemma":0.01142212,"domain_scores_codex":[0.9984959,0.0001873014,0.0002471923,0.0004652412,0.0004403948,0.000163833],"domain_scores_gemma":[0.9982178,0.0004294138,0.0002765197,0.0003651241,0.0004757336,0.0002353624],"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":[0.003467015,0.0005033421,0.01622473,0.01517746,0.001141647,0.0009489559,0.0001631324,0.002615124,0.02954396,0.001543334,0.8947346,0.03393686],"study_design_scores_gemma":[0.0007480717,0.0002950318,0.05873272,0.000837107,0.0006268804,0.0008831082,0.0001760899,0.001867627,0.01167732,0.002333677,0.921681,0.0001413768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002650641,0.0006445737,0.0003867096,0.00007846492,0.00003096833,0.00003684648,0.9950145,0.0004040431,0.0007531954],"genre_scores_gemma":[0.001133315,0.0001531304,0.0006059805,0.00002827686,0.000004999004,0.00006831488,0.9977996,0.00002459169,0.0001817867],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01424051,"threshold_uncertainty_score":0.04763925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04590767023424563,"score_gpt":0.3299153693218273,"score_spread":0.2840076990875817,"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."}}