{"id":"W2150547367","doi":"10.1093/bioinformatics/btu786","title":"Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; National Institute of General Medical Sciences; Canadian Institute for Advanced Research","keywords":"Inference; Scalability; Computational biology; Compendium; Computer science; Genome; Biology; Software; Biological data; Source code; Data type; Bioinformatics; Gene; Database; Genetics; Artificial intelligence; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005052554,0.0001206859,0.0001470875,0.00004265209,0.00006746487,0.00003757614,0.00052811,0.00007959269,0.000007164334],"category_scores_gemma":[0.0001647152,0.00008274095,0.00003372163,0.0000972889,0.00006109578,0.00002483681,0.0002282637,0.00009911034,0.000004111558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000875013,"about_ca_system_score_gemma":0.0000609919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001810328,"about_ca_topic_score_gemma":0.0001091369,"domain_scores_codex":[0.9991627,0.00001941517,0.0004621382,0.0001098179,0.00008911378,0.0001568392],"domain_scores_gemma":[0.9988474,0.00007495028,0.0002178354,0.0007370891,0.00008998874,0.00003269033],"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":[0.0001047504,0.0001543051,0.0002725086,0.0003892332,0.0001644041,7.669463e-8,0.001983295,0.003424562,0.02277583,0.0100395,0.01059863,0.9500929],"study_design_scores_gemma":[0.0004590041,0.0001532447,0.0001391816,0.00002252377,0.00003106607,0.000005011794,0.001046338,0.914503,0.02389645,0.0002764108,0.0592864,0.0001813377],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001258259,0.00006622909,0.996527,0.00007759806,0.0001448404,0.0003852191,0.0001822484,0.000006262114,0.001352372],"genre_scores_gemma":[0.896203,0.0000709232,0.1010514,0.0001809892,0.0001347566,0.00003278823,0.002151359,0.00001262396,0.0001621316],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9499115,"threshold_uncertainty_score":0.3374078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03736471980374437,"score_gpt":0.2869714272434773,"score_spread":0.2496067074397329,"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."}}