{"id":"W2105187843","doi":"10.1093/bioinformatics/btr288","title":"The role of indirect connections in gene networks in predicting function","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Institutes of Health; Michael Smith Health Research BC","keywords":"Computer science; Function (biology); Data mining; Code (set theory); Gene regulatory network; Source code; MATLAB; Algorithm; Theoretical computer science; Machine learning; Artificial intelligence; Gene; Gene expression; Programming language; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003424303,0.0009196269,0.0004232662,0.001815434,0.0008347036,0.001602145,0.0009331133,0.0006847405,0.00325807],"category_scores_gemma":[0.0284798,0.0004067985,0.0003358529,0.002010367,0.002039775,0.003395634,0.001588502,0.001137735,0.0006990267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007027967,"about_ca_system_score_gemma":0.0006671005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282092,"about_ca_topic_score_gemma":0.003008641,"domain_scores_codex":[0.9989535,0.0004735697,0.00005882307,0.0002830142,0.0001797653,0.000051295],"domain_scores_gemma":[0.9756991,0.02072919,0.00127669,0.001010052,0.0009445869,0.000340442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007195104,0.0001618618,0.2347094,0.001473917,0.0004002259,0.0006454377,0.000953504,0.3175242,0.01543832,0.1275848,0.005566301,0.2948225],"study_design_scores_gemma":[0.00004418988,0.0001489348,0.0420522,0.0002002942,0.0001961358,0.000711666,0.0001295276,0.6313758,0.008128515,0.3130166,0.003939035,0.0000571388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3720003,0.003886216,0.6072909,0.00285631,0.00007043293,0.0001151984,0.001681035,0.0008234322,0.0112762],"genre_scores_gemma":[0.925688,0.001400742,0.07030648,0.0001223849,0.00008442666,0.0001002321,0.0007967225,0.0001385327,0.001362511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003424303,"threshold_uncertainty_score":0.01810968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009714114048023536,"score_gpt":0.1941080902456587,"score_spread":0.1843939761976352,"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."}}