{"id":"W2146645587","doi":"10.1186/1756-9966-29-120","title":"An algorithm to discover gene signatures with predictive potential","year":2010,"lang":"en","type":"article","venue":"Journal of Experimental & Clinical Cancer Research","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; McMaster University Medical Centre","funders":"Stem Cell Network","keywords":"Breast cancer; Gene expression profiling; Gene; Gene expression; Gene signature; Computational biology; Algorithm; Machine learning; Computer science; Bioinformatics; Cancer; Oncology; Medicine; Biology; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007665599,0.0001644015,0.0002769096,0.00008602526,0.0001534777,0.00005640922,0.0004095216,0.0001568174,0.0001162694],"category_scores_gemma":[0.0000788585,0.0001124746,0.0001466206,0.0001393784,0.0003990208,0.00002254084,0.0001949961,0.0007311819,0.00000447866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006801214,"about_ca_system_score_gemma":0.0004209312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001365987,"about_ca_topic_score_gemma":0.0001102627,"domain_scores_codex":[0.9979213,0.0002025307,0.0004034067,0.0003791196,0.0007301182,0.0003635174],"domain_scores_gemma":[0.9985667,0.00005014992,0.0001394562,0.0003098669,0.0005325043,0.0004012639],"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":[0.004893934,0.001109891,0.009130671,0.000002438351,0.0004017168,0.00007513765,0.0001781682,0.0001014059,0.9726381,0.000008943556,0.003242396,0.008217165],"study_design_scores_gemma":[0.003340618,0.01075887,0.07238746,0.00003897601,0.00004851608,0.00009764095,0.0008621846,0.00003809817,0.9061897,0.0000311423,0.005962741,0.0002439871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962097,0.001568286,0.0005677817,0.0005510607,0.0006676597,0.0002305925,0.00007349893,0.000004034792,0.0001274138],"genre_scores_gemma":[0.9915196,0.0002326158,0.00458654,0.0002680205,0.003211637,0.00005376077,0.00001180149,0.00003146434,0.0000846066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06644837,"threshold_uncertainty_score":0.458658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842146312314467,"score_gpt":0.4800361137865739,"score_spread":0.4416146506634293,"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."}}