{"id":"W1080308004","doi":"10.1016/j.jmb.2015.07.021","title":"Studying Cellular Signal Transduction with OMIC Technologies","year":2015,"lang":"en","type":"review","venue":"Journal of Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Breast Cancer Alliance; National Cancer Institute, Cairo University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; Richard and Susan Smith Family Foundation","keywords":"Systems biology; Signal transduction; Computational biology; Computer science; Biology; SIGNAL (programming language); Cell biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007889712,0.0005370467,0.001796192,0.0004453649,0.00005779933,0.00002473008,0.0007153101,0.0009319204,0.00001401131],"category_scores_gemma":[0.00004402145,0.0003753567,0.0008885016,0.0003949905,0.0002455301,0.000004561953,0.0001319111,0.0005510397,0.000006327106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009002587,"about_ca_system_score_gemma":0.0006740058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001382519,"about_ca_topic_score_gemma":0.0000025108,"domain_scores_codex":[0.997426,0.0005001961,0.0009348842,0.0004943733,0.0002552338,0.0003892844],"domain_scores_gemma":[0.9976279,0.00001551443,0.001282528,0.0005635367,0.0003955114,0.0001150586],"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":[0.0001093078,0.000142061,0.00002928819,0.0009044828,0.00379167,0.0002292967,0.00001549926,0.0003288825,0.05157182,0.00004138074,0.0007513065,0.942085],"study_design_scores_gemma":[0.0004926175,0.00156658,2.818961e-7,0.0006847165,0.002541741,0.00115418,0.00008028571,0.000003391916,0.01063373,0.0001253691,0.9822392,0.0004778984],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002716822,0.9776275,0.01905671,0.00004333149,0.0001993943,0.000283953,0.000009839498,0.00001732916,0.00004508921],"genre_scores_gemma":[0.005506017,0.9913974,0.002379276,0.00001926066,0.0004096527,0.00001772865,0.000125917,0.00008858518,0.00005617588],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9814879,"threshold_uncertainty_score":0.9998698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02356586378835912,"score_gpt":0.2910461617834173,"score_spread":0.2674802979950582,"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."}}