{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343836,0.0001140341,0.0001159979,0.00004945258,0.0001980751,0.0001107715,0.0001286573,0.00009204531,0.0001207555],"category_scores_gemma":[0.00001531001,0.0001020599,0.000083169,0.0001554825,0.0001155088,0.000008538726,0.00004998756,0.00005588144,0.00001137118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002208719,"about_ca_system_score_gemma":0.00006050836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003791515,"about_ca_topic_score_gemma":0.000008582099,"domain_scores_codex":[0.9985679,0.00002339471,0.0005667727,0.000362414,0.0002495923,0.0002299426],"domain_scores_gemma":[0.9985356,0.000004684885,0.0004894253,0.0005937253,0.0003027919,0.00007374103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001617203,0.0002330476,0.05503245,0.00006004633,0.0000437348,0.000001850477,0.00006397892,0.05485046,0.8287781,0.00006204678,0.05545139,0.005406641],"study_design_scores_gemma":[0.001500824,0.0004778619,0.2575009,0.0004143064,0.0001257097,0.00002818318,0.0001020584,0.06053664,0.6496922,0.01115675,0.01697532,0.001489275],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960553,0.00004399853,0.001063895,0.00004457609,0.002043828,0.0003280102,0.00001199212,0.00000872963,0.0003996757],"genre_scores_gemma":[0.9987319,0.000001407219,0.0001277697,0.00007487434,0.0001628738,0.00003469356,0.000278881,0.0000128572,0.0005747976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2024685,"threshold_uncertainty_score":0.416188,"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."}}