{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007544298,0.0002840509,0.0006030471,0.001158045,0.0001759966,0.0003806074,0.000255586,0.000305305,0.0003918038],"category_scores_gemma":[0.002123792,0.0001347626,0.0002713013,0.0006304177,0.0001362391,0.0002446134,0.0001841942,0.0002863207,0.0002272233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002115103,"about_ca_system_score_gemma":0.0003789208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004355405,"about_ca_topic_score_gemma":0.0006328796,"domain_scores_codex":[0.9997416,0.00009611238,0.0000243387,0.0000313648,0.00007728748,0.00002922876],"domain_scores_gemma":[0.9995782,0.0001870675,0.00007701558,0.00002591836,0.0001050946,0.00002677821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001863248,0.0005107507,0.1872289,0.0003900603,0.0001548934,0.0005193003,0.0001329478,0.008167645,0.2517981,0.0005078148,0.00204503,0.5466813],"study_design_scores_gemma":[0.0002352593,0.002704601,0.3569034,0.00007676687,0.000402791,0.004417141,0.0002661631,0.2668942,0.3575,0.003038717,0.007475641,0.00008542086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9496003,0.004049665,0.04457928,0.0003239971,0.00003768045,0.0001018324,0.0003150339,0.0004087758,0.0005832718],"genre_scores_gemma":[0.9525061,0.0007440835,0.04586175,0.00004995534,0.0000212707,0.00004469067,0.0003486801,0.00001070014,0.0004127758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001158045,"threshold_uncertainty_score":0.003989875,"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."}}