{"id":"W2055451826","doi":"10.1016/j.canlet.2006.10.012","title":"Identification of PEG10 as a progression related biomarker for hepatocellular carcinoma","year":2006,"lang":"en","type":"article","venue":"Cancer Letters","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biology; Hepatocellular carcinoma; Comparative genomic hybridization; HCCS; Candidate gene; Gene; Cancer research; Biomarker; Microarray; Copy-number variation; Microarray analysis techniques; Gene expression; Gene expression profiling; Population; Genetics; Complementary DNA; Genome; Computational biology; Medicine","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":[0.00007484611,0.00007571397,0.00007438482,0.00002801758,0.00005201578,0.00001096829,0.00007966586,0.00006375925,0.00001812871],"category_scores_gemma":[0.000007195289,0.00007296698,0.00007011658,0.00005016215,0.00004740802,0.000003452504,0.00001919783,0.00002003826,0.000002826441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001915889,"about_ca_system_score_gemma":0.00004174024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009883258,"about_ca_topic_score_gemma":0.000005762952,"domain_scores_codex":[0.9994102,0.0000171137,0.0002218472,0.0001737781,0.00005988497,0.0001171716],"domain_scores_gemma":[0.9996243,0.000003669391,0.0001330053,0.0001605195,0.00006101957,0.00001745502],"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":[0.00003771872,0.00002014882,0.05246671,0.00003654702,0.00002192228,6.682425e-7,0.00003525667,0.00006020439,0.944044,0.0002169904,0.002729532,0.000330303],"study_design_scores_gemma":[0.0004763355,0.00004862167,0.03496138,0.00001333448,0.00003103217,0.000005026461,0.00002857176,0.0003918179,0.9537246,0.0001201524,0.01007952,0.0001196111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967847,0.0007211246,0.001220819,0.0007086947,0.0001532184,0.0002447178,0.00004859187,0.000006744989,0.0001113998],"genre_scores_gemma":[0.9985318,0.00001482886,0.0001465058,0.00009749227,0.00009721787,0.000137374,0.0002207768,0.00001317798,0.0007408181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01750532,"threshold_uncertainty_score":0.2975507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007858131024098144,"score_gpt":0.2448499992292478,"score_spread":0.2369918682051497,"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."}}