{"id":"W2101370506","doi":"10.1016/j.cell.2013.11.003","title":"Extracting Insight from Noisy Cellular Networks","year":2013,"lang":"en","type":"article","venue":"Cell","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Planning and Budgeting Committee of the Council for Higher Education of Israel; Israeli Centers for Research Excellence; Canadian Institutes of Health Research","keywords":"Biology; Common descent; Meaning (existential); Categorization; Analogy; Organism; Cognitive science; Adaptation (eye); Evolutionary biology; Gene regulatory network; Epistemology; Artificial intelligence; Computer science; Gene; Genetics; Neuroscience; Philosophy; Psychology","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":[0.0008277127,0.001008518,0.001031213,0.003264686,0.0003897396,0.001461415,0.0007895424,0.001043274,0.0008518496],"category_scores_gemma":[0.01006685,0.0005779898,0.0007170355,0.002375962,0.0006093712,0.002098086,0.001152073,0.001009641,0.000478513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006568397,"about_ca_system_score_gemma":0.000643505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737992,"about_ca_topic_score_gemma":0.002941695,"domain_scores_codex":[0.9993805,0.0001802492,0.00004023258,0.0001716309,0.0001806299,0.00004680607],"domain_scores_gemma":[0.9923807,0.005695086,0.0005694766,0.0007917446,0.0004321542,0.0001307895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001049437,0.000286122,0.06033635,0.001130286,0.0005484195,0.002862255,0.0005710268,0.5851712,0.05442673,0.0448341,0.009835209,0.238949],"study_design_scores_gemma":[0.00002047492,0.0000360992,0.005676865,0.00004992495,0.0001043093,0.0003632189,0.0001093931,0.8863081,0.006657769,0.09722402,0.003432873,0.0000168022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2263782,0.002829851,0.7570916,0.002094958,0.0001009705,0.00006845559,0.006847532,0.002376641,0.002211713],"genre_scores_gemma":[0.8496997,0.002075035,0.1389463,0.0002387862,0.0002046387,0.00008063451,0.007608908,0.0001509552,0.0009950479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003264686,"threshold_uncertainty_score":0.004765749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005567392057436057,"score_gpt":0.1818646102021055,"score_spread":0.1762972181446695,"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."}}