{"id":"W2946233355","doi":"10.1504/ijdmb.2019.099714","title":"Effective induction of gene regulatory networks using a novel recommendation method","year":2019,"lang":"en","type":"article","venue":"International Journal of Data Mining and Bioinformatics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Computer science; Data mining; Inference; Set (abstract data type); Receiver operating characteristic; Gene regulatory network; Data set; In silico; Precision and recall; Measure (data warehouse); Collaborative filtering; Machine learning; Artificial intelligence; Gene; Recommender system; Biology; 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":[0.0008579244,0.00008652148,0.0001652106,0.000134002,0.00002148468,0.00002604345,0.0002876139,0.00008798116,0.000008809964],"category_scores_gemma":[0.00006497533,0.00007626042,0.00005607616,0.00007597454,0.00002790984,0.00006313335,0.0001800838,0.00006892771,2.609461e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000214617,"about_ca_system_score_gemma":0.00005039018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003693568,"about_ca_topic_score_gemma":0.000001754621,"domain_scores_codex":[0.9991135,0.00004126073,0.0004774108,0.00009971457,0.0001900771,0.00007805401],"domain_scores_gemma":[0.9986532,0.00003561185,0.0007470602,0.0002078836,0.0003176913,0.00003853651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002633313,0.00008176952,0.007243915,0.00003487353,0.00132708,8.622097e-7,0.0002900941,0.01294227,0.2691423,0.00001132739,0.0006061386,0.708056],"study_design_scores_gemma":[0.001768387,0.0004048743,0.005710371,0.0002060749,0.0002577341,0.0007011971,0.001018224,0.9406303,0.04654967,0.000009522332,0.002495082,0.0002485348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6224362,0.000139636,0.376903,0.00003198357,0.000355221,0.0000512203,0.00003505193,0.000001252991,0.0000464383],"genre_scores_gemma":[0.6140057,0.00009005385,0.3852705,0.00004399825,0.0002968514,2.614238e-7,0.0002751443,0.000006604562,0.00001089053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9276881,"threshold_uncertainty_score":0.310981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02846873594889496,"score_gpt":0.3166203865312484,"score_spread":0.2881516505823534,"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."}}