{"id":"W3208513665","doi":"10.5281/zenodo.1303838","title":"Preprocessing scripts and data for study: Pathway-Based Subnetworks Enable Cross-Disease Biomarker Discovery","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Biomarker discovery; Scripting language; Preprocessor; Biomarker; Computer science; Computational biology; Disease; Data mining; Artificial intelligence; Biology; Medicine; Proteomics; Programming language; Gene; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515405,0.002274378,0.001392938,0.003118524,0.0007580955,0.002504077,0.002445552,0.001359583,0.2259215],"category_scores_gemma":[0.007234647,0.0007888784,0.001586993,0.003454041,0.0003662058,0.001274661,0.001804216,0.001915197,0.08370635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166509,"about_ca_system_score_gemma":0.002419413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005455784,"about_ca_topic_score_gemma":0.01294695,"domain_scores_codex":[0.9992257,0.0001078571,0.0001006723,0.0003130943,0.0001415667,0.0001111265],"domain_scores_gemma":[0.9967865,0.001532025,0.0002638281,0.0006676065,0.0004901438,0.000260011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003200731,0.00005947917,0.003084621,0.001935767,0.0001227608,0.00008786856,0.00005335743,0.0007601088,0.001035381,0.001381364,0.984501,0.006658208],"study_design_scores_gemma":[0.000853595,0.00007134563,0.01227691,0.0005572286,0.0001815613,0.0003109765,0.0001152805,0.002163747,0.003345029,0.01114613,0.9689072,0.0000709364],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002247905,0.00003569922,0.0005853638,0.00003758318,0.0000193116,0.00003828984,0.9968013,0.001620799,0.0006369475],"genre_scores_gemma":[0.001435362,0.00005368442,0.00274886,0.00008117276,0.0000101219,0.0003571346,0.9937343,0.0006359143,0.0009435276],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2259215,"threshold_uncertainty_score":0.7557828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05866729588260273,"score_gpt":0.3258709877965938,"score_spread":0.2672036919139911,"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."}}