{"id":"W2047211642","doi":"10.1371/journal.pone.0088163","title":"Use of Sequenom Sample ID Plus® SNP Genotyping in Identification of FFPE Tumor Samples","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Ontario Institute for Cancer Research","funders":"Ontario Ministry of Research and Innovation; Prostate Cancer Canada; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Genotyping; Molecular Inversion Probe; Microsatellite; SNP genotyping; Biology; Amplicon; Polymerase chain reaction; DNA profiling; STR analysis; STR multiplex system; Single-nucleotide polymorphism; DNA extraction; SNP array; Genetics; Genotype; Computational biology; DNA; Multiplex polymerase chain reaction; Gene; Allele","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":[0.0001916269,0.0000818331,0.0001758449,0.00005378356,0.00002350899,0.000005849509,0.0001513599,0.00008912229,0.00001022342],"category_scores_gemma":[0.0002640376,0.00008656608,0.00004099957,0.00008805989,0.00008265882,0.000003139223,0.0000719939,0.00005116216,0.000001686593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001089259,"about_ca_system_score_gemma":0.00003124198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002523104,"about_ca_topic_score_gemma":0.00007870058,"domain_scores_codex":[0.9991941,0.00005827574,0.0003295245,0.0002224513,0.00007777286,0.000117882],"domain_scores_gemma":[0.9992471,0.00003291593,0.0001859817,0.0004150151,0.00009284916,0.00002608585],"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":[0.00001907754,0.0001907026,0.004740342,0.00004893088,0.00003405836,7.872477e-8,0.00001564897,0.00002501034,0.9925281,0.001978924,0.00002624155,0.0003929129],"study_design_scores_gemma":[0.0001262309,0.00008636402,0.006770273,0.00003199889,0.00002477122,6.090007e-7,0.000006931924,0.0004302,0.9904202,0.0006130096,0.001399884,0.00008949117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9567808,0.0001004635,0.04263287,0.00007668955,0.000005666021,0.0002427581,0.00009414105,0.00001056792,0.00005608142],"genre_scores_gemma":[0.9727714,0.00009632154,0.02656011,0.00008906984,0.00002941697,0.00005523689,0.000359178,0.00001334115,0.00002590622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01607275,"threshold_uncertainty_score":0.3530062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07264150466602563,"score_gpt":0.2693159023288592,"score_spread":0.1966743976628336,"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."}}