{"id":"W2156716434","doi":"10.3389/fcell.2014.00038","title":"Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks","year":2014,"lang":"en","type":"review","venue":"Frontiers in Cell and Developmental Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":299,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"Standortagentur Tirol","keywords":"Gene regulatory network; Inference; Computer science; Consistency (knowledge bases); Context (archaeology); Popularity; Data science; Perspective (graphical); Biological network; Artificial intelligence; Computational biology; Gene; Biology; Gene expression; Genetics; Psychology","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":[0.00151248,0.001219221,0.001416141,0.003148502,0.0003697466,0.00169098,0.001532219,0.001930527,0.002037514],"category_scores_gemma":[0.002491791,0.0004243208,0.0006635484,0.003417898,0.002318604,0.003216331,0.001065183,0.00259419,0.001076067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001633233,"about_ca_system_score_gemma":0.001289583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850042,"about_ca_topic_score_gemma":0.001822326,"domain_scores_codex":[0.9995353,0.0001519474,0.00003512937,0.00011007,0.0001444473,0.00002305927],"domain_scores_gemma":[0.9985188,0.001101117,0.00007632178,0.00005391424,0.0002036754,0.0000462233],"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":[0.0000305905,0.00004996408,0.0004779737,0.00726012,0.0001393521,0.0001985128,0.0002345772,0.006673623,0.001389197,0.1895046,0.01727209,0.7767693],"study_design_scores_gemma":[0.000009610222,0.00005296906,0.00101245,0.003421858,0.00009179391,0.001108878,0.0002025566,0.004661275,0.0008551708,0.2076017,0.7809135,0.00006823041],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003081253,0.9820789,0.01264298,0.001794272,0.0003394603,0.00001064569,0.00004132681,0.00002903891,0.00275531],"genre_scores_gemma":[0.004383043,0.9872378,0.006051721,0.000460395,0.00061989,0.00002694809,0.00006938339,0.000008296515,0.001142535],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003148502,"threshold_uncertainty_score":0.01184994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937954923349737,"score_gpt":0.2398174508603861,"score_spread":0.2204379016268887,"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."}}