{"id":"W2977572999","doi":"10.1101/793166","title":"RCy3: Network Biology using Cytoscape from within R","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Bioconductor; Workflow; Computer science; Interoperability; Automation; Network analysis; Set (abstract data type); Power graph analysis; Graph; Software engineering; World Wide Web; Theoretical computer science; Database; Biology; Programming language; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005759938,0.00296534,0.003433536,0.00551258,0.001759941,0.005031361,0.006216088,0.001760073,0.1198203],"category_scores_gemma":[0.02607376,0.002172711,0.003029577,0.004531743,0.001356089,0.003750272,0.004865355,0.005766274,0.05662213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001594638,"about_ca_system_score_gemma":0.005841827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008355937,"about_ca_topic_score_gemma":0.00894904,"domain_scores_codex":[0.9961227,0.001050432,0.000235476,0.0009964494,0.001329201,0.0002657127],"domain_scores_gemma":[0.9908044,0.005327657,0.0005911568,0.001590981,0.001111666,0.0005740983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003723372,0.00006080086,0.002157161,0.004203885,0.001086955,0.0005967875,0.0004764977,0.01227465,0.005996383,0.04534705,0.8779809,0.0494466],"study_design_scores_gemma":[0.0009602106,0.00007845584,0.002959389,0.0008669214,0.0005856024,0.001070603,0.0001548485,0.08413964,0.01724099,0.1301466,0.7613839,0.0004129121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002841113,0.001419959,0.4449005,0.002171746,0.001266469,0.0004378194,0.1129475,0.4228283,0.01118643],"genre_scores_gemma":[0.06191877,0.002538016,0.5703424,0.002454123,0.0005799104,0.005084498,0.1616077,0.1808377,0.01463693],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1198203,"threshold_uncertainty_score":0.4008387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224279546648248,"score_gpt":0.2186138715220999,"score_spread":0.2063710760556174,"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."}}