{"id":"W2095349462","doi":"10.1038/srep03368","title":"Identification of breast cancer patients based on human signaling network motifs","year":2013,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Breast cancer; Computational biology; Disease; Gene; Cancer; Gene selection; Gene expression profiling; Bioinformatics; Feature selection; Gene expression; Medicine; Computer science; Biology; Machine learning; Internal medicine; Genetics; Microarray analysis techniques","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":[0.0002343098,0.0002669267,0.0003080084,0.001332046,0.0001621007,0.0002693299,0.0001560093,0.0002458584,0.0009310761],"category_scores_gemma":[0.001165115,0.00006917109,0.0002135289,0.0006634851,0.00009216775,0.0001618557,0.0002099337,0.0002066811,0.0002298354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001769485,"about_ca_system_score_gemma":0.0001925287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007238815,"about_ca_topic_score_gemma":0.001805882,"domain_scores_codex":[0.9998429,0.0000431561,0.00001544206,0.00003940486,0.00003367225,0.00002529778],"domain_scores_gemma":[0.9996647,0.0001500082,0.00007661866,0.00001902682,0.00005260301,0.00003707974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001114102,0.0002477584,0.6842961,0.0001700308,0.0001531095,0.0008256471,0.0001335445,0.008243545,0.09009831,0.001054991,0.002226759,0.2114362],"study_design_scores_gemma":[0.00007855829,0.0004770255,0.6516384,0.00005753002,0.0002226312,0.004368303,0.000362116,0.2768947,0.05313362,0.005943365,0.006762809,0.00006096435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743726,0.0004396217,0.02193137,0.0001908431,0.00001477519,0.00009212214,0.001520885,0.0001751154,0.001262723],"genre_scores_gemma":[0.9825062,0.0001297618,0.0157654,0.00003638807,0.00001195332,0.00004311471,0.001210731,0.000007536335,0.0002889911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001332046,"threshold_uncertainty_score":0.00311476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007175444552192956,"score_gpt":0.2329593802808104,"score_spread":0.2257839357286175,"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."}}