{"id":"W1877439477","doi":"10.1002/0471250953.bi0813s23","title":"Exploring Biological Networks with Cytoscape Software","year":2008,"lang":"en","type":"article","venue":"Current Protocols in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; Unilever","keywords":"Interconnectivity; Biological network; Context (archaeology); Computer science; Computational biology; Software; Gene regulatory network; Genomics; Bioinformatics; Data science; Biology; Gene; Gene expression; Artificial intelligence; Genome; Genetics","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":[0.0002540782,0.0003040179,0.0002800872,0.00008776112,0.0001582937,0.00004116917,0.0003781698,0.0001729463,0.00001160126],"category_scores_gemma":[0.00004773746,0.0002290946,0.00008610493,0.0002520824,0.0001947867,0.00004038209,0.0002247653,0.0003395,0.00002283751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003142897,"about_ca_system_score_gemma":0.0001056927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001246179,"about_ca_topic_score_gemma":0.00000525315,"domain_scores_codex":[0.9983619,0.00003107362,0.0006473729,0.0002278711,0.0001920475,0.0005396681],"domain_scores_gemma":[0.9990559,0.00001955383,0.0002196318,0.0004863453,0.00007737536,0.000141168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002428185,0.001655094,0.321647,0.001654053,0.0002827098,0.00006691835,0.003928338,0.05840489,0.0007949097,0.001487096,0.01647434,0.5911765],"study_design_scores_gemma":[0.01835867,0.00781329,0.06653005,0.00307533,0.00005823932,0.001239226,0.001413992,0.1505912,0.008336242,0.0004954817,0.7358016,0.006286717],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4542986,0.000800047,0.4864329,0.00005680676,0.0006899195,0.05467031,0.00005022418,0.0002130677,0.0027881],"genre_scores_gemma":[0.7153703,0.004206271,0.1545445,0.0007888196,0.001721452,0.1219032,0.001142422,0.0001690091,0.0001540251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7193273,"threshold_uncertainty_score":0.9342209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1234336037324784,"score_gpt":0.2955983794445602,"score_spread":0.1721647757120818,"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."}}