{"id":"W2037625634","doi":"10.1142/s0219720006002399","title":"PROTEOMIC BIOMARKER IDENTIFICATION FOR DIAGNOSIS OF EARLY RELAPSE IN OVARIAN CANCER","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's Health Research Institute","funders":"National Science Foundation","keywords":"Markov blanket; Feature selection; Support vector machine; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); Feature vector; Biomarker discovery; Biomarker; Curse of dimensionality; Machine learning; Identification (biology); Markov chain; Proteomics; Markov model; Biology","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":[0.0001643144,0.00006342452,0.0001524372,0.0001074955,0.00003372741,0.00001148893,0.0000846939,0.00007574931,0.00001114393],"category_scores_gemma":[0.00002055669,0.00005488031,0.00004926947,0.00007905176,0.00005578341,0.00009499652,0.00001668877,0.00007074267,2.649907e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003047078,"about_ca_system_score_gemma":0.0000505149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004033224,"about_ca_topic_score_gemma":0.000003018405,"domain_scores_codex":[0.9991208,0.000005427759,0.000687585,0.00005375602,0.00005425725,0.0000782085],"domain_scores_gemma":[0.9989685,0.00009395341,0.0006854217,0.00005187582,0.0001801242,0.00002007141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007435241,0.0007155718,0.4504823,0.00103963,0.0002282904,0.00000202815,0.000758633,0.01699517,0.2230283,0.1968301,0.001301698,0.1078747],"study_design_scores_gemma":[0.003622414,0.0003178154,0.1268157,0.0003394679,0.00007887297,0.00006419711,0.0001220519,0.08739066,0.1306704,0.6474863,0.002669125,0.000423056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.817774,0.0001189065,0.1813758,0.0003441703,0.00002255215,0.0001535818,0.000101494,0.000004040208,0.0001054639],"genre_scores_gemma":[0.8805007,0.0001026108,0.1192018,0.00001875271,0.00003339351,0.00008659015,0.00003787927,0.000004488163,0.000013724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4506561,"threshold_uncertainty_score":0.2237954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356187905384033,"score_gpt":0.2921849387862475,"score_spread":0.2786230597324071,"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."}}