{"id":"W2950788368","doi":"10.1080/15384101.2017.1361068","title":"Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data","year":2017,"lang":"en","type":"article","venue":"Cell Cycle","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Russian Academy of Sciences","keywords":"Biology; Biomedicine; Computational biology; Transcriptome; Experimental data; Gene expression; Gene; Stability (learning theory); Bioinformatics; Computer science; Genetics; Machine learning; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000189636,0.0000670278,0.00007671786,0.000007064531,0.0001006641,0.00001464825,0.0007272739,0.00005123262,0.0000122894],"category_scores_gemma":[0.00003280734,0.00005158349,0.00001851047,0.00001008996,0.0001866476,0.00001280977,0.0005925413,0.00003062675,5.354279e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006106934,"about_ca_system_score_gemma":0.00004740568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004950962,"about_ca_topic_score_gemma":0.00003060363,"domain_scores_codex":[0.9993542,0.00003695312,0.0001420714,0.000315349,0.00008119313,0.00007020566],"domain_scores_gemma":[0.9974917,0.000003066001,0.0001779676,0.002277265,0.00002477896,0.00002517617],"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":[0.00005586279,0.00004872933,0.0003975258,0.00002699767,0.000007238792,7.176323e-8,0.00006915034,7.317416e-7,0.996955,0.000006462265,0.0003139003,0.002118292],"study_design_scores_gemma":[0.0003761344,0.0000482783,0.007067187,0.000007181458,0.00001045597,5.720352e-7,0.0001568803,0.0003602665,0.9910131,0.00002202381,0.0008817014,0.00005627865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967896,0.001403247,0.0005712316,0.00007293199,0.00005417824,0.0002380715,0.0005588965,0.000001808388,0.0003100238],"genre_scores_gemma":[0.999082,0.00009632818,0.0002698969,0.00001442225,0.00001937248,0.00001102764,0.0004417996,0.000007635274,0.00005750765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006669661,"threshold_uncertainty_score":0.2103514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070377259867455,"score_gpt":0.3088575327138479,"score_spread":0.2018198067271024,"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."}}