{"id":"W3138046340","doi":"10.1101/2021.03.15.435515","title":"BIONIC: Biological Network Integration using Convolutions","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University Health Network; University of Toronto","funders":"National Center for Research Resources; National Human Genome Research Institute; Genome Canada; Canadian Institutes of Health Research; National Institutes of Health; Ministero dello Sviluppo Economico","keywords":"Computer science; Scalability; Biological network; Artificial intelligence; Systems biology; Annotation; Weighting; ENCODE; Leverage (statistics); Machine learning; Computational biology; Biology; Gene","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":[0.0008251785,0.0009898084,0.0006061164,0.001512017,0.0004298677,0.0009805836,0.001450288,0.0008357458,0.004705761],"category_scores_gemma":[0.001964119,0.0004463826,0.0008224291,0.00103864,0.0005710212,0.001737096,0.001492368,0.001139489,0.0008259621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279796,"about_ca_system_score_gemma":0.001039402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00763528,"about_ca_topic_score_gemma":0.0066333,"domain_scores_codex":[0.999647,0.00005437982,0.00001739745,0.0001196097,0.0001227243,0.00003882065],"domain_scores_gemma":[0.9994961,0.0001573608,0.00007745619,0.000107836,0.0001147529,0.00004646022],"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":[0.0002334152,0.0001624979,0.003792327,0.0001713086,0.0002276729,0.0002306534,0.0001065305,0.6450188,0.02186137,0.03354694,0.009014071,0.2856343],"study_design_scores_gemma":[0.000004242883,0.000008854943,0.0001690869,0.000003298643,0.000005468311,0.00001822001,0.000003146926,0.9925892,0.002189132,0.004129197,0.0008763432,0.000003741033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02330569,0.0002010372,0.9616371,0.000204488,0.00007627391,0.00005151652,0.0003474972,0.01176163,0.002414661],"genre_scores_gemma":[0.4411514,0.0002399098,0.550914,0.0002499544,0.0000816041,0.0001691338,0.001876909,0.0008633183,0.004453725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00763528,"threshold_uncertainty_score":0.0157423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054091321391982,"score_gpt":0.2300527916188266,"score_spread":0.2095118784049068,"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."}}