{"id":"W2163614459","doi":"10.3892/or.2014.2979","title":"Glycan profiling of gestational choriocarcinoma using a lectin microarray","year":2014,"lang":"en","type":"article","venue":"Oncology Reports","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Keio University","keywords":"Choriocarcinoma; Biology; Carcinogenesis; Glycan; Lectin; Microarray analysis techniques; Cancer research; Galectin; Microarray; Trophoblast; Immunology; Molecular biology; Placenta; Glycoprotein; Cancer; Biochemistry; Fetus; Genetics; Gene expression; Pregnancy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001449552,0.000303508,0.0002589488,0.0005315611,0.0002167521,0.0002372473,0.0001593589,0.0003491437,0.0005507465],"category_scores_gemma":[0.0001632964,0.0001146652,0.0002352623,0.0005363917,0.0001090462,0.0001715526,0.0002217735,0.0003119412,0.0001947966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002327201,"about_ca_system_score_gemma":0.0001682233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277996,"about_ca_topic_score_gemma":0.002323508,"domain_scores_codex":[0.9998733,0.00001993721,0.000008500809,0.00003323111,0.00003977854,0.0000252269],"domain_scores_gemma":[0.9999198,0.00001590274,0.00001529407,0.000008370332,0.00002475385,0.00001588454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004701718,0.00001010465,0.001094419,0.00002670706,0.000007017893,0.00003668017,0.00001637283,0.00009250902,0.9963701,0.00002205779,0.00004144249,0.002235548],"study_design_scores_gemma":[0.000008388954,0.0004005547,0.09624817,0.00001281788,0.00007902681,0.0008963672,0.0001598791,0.009439739,0.8883132,0.0001669491,0.004242179,0.00003282113],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744889,0.001454128,0.01982901,0.0001684917,0.00003011461,0.00007303834,0.001848223,0.0003358655,0.001772203],"genre_scores_gemma":[0.9375688,0.002017575,0.05377323,0.0002358999,0.00002035042,0.0001580106,0.003445266,0.0000391332,0.002741736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001277996,"threshold_uncertainty_score":0.002541065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120649645125814,"score_gpt":0.3206320014384328,"score_spread":0.2994255049871747,"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."}}