{"id":"W4297216344","doi":"10.1093/dote/doac051.328","title":"328. MOLECULAR SUBTYPING OF ESOPHAGEAL ADENOCARCINOMA BY NON-NEGATIVE MATRIX FACTORIZATION OF LASER CAPTURE MICRODISSECTED RNA-SEQ SAMPLES","year":2022,"lang":"en","type":"article","venue":"Diseases of the Esophagus","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Laser capture microdissection; Adenocarcinoma; Gene expression; Microdissection; Medicine; Transcriptome; Biopsy; Gene expression profiling; Esophagus; RNA; Pathology; Non-negative matrix factorization; Barrett's esophagus; Carcinoma; Cancer research; Gene; Cancer; Biology; Internal medicine; Matrix decomposition","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":[0.0004785184,0.0003988831,0.0003345943,0.0008250217,0.0005009479,0.000452345,0.0001998145,0.0003994535,0.0018144],"category_scores_gemma":[0.001111428,0.0001785471,0.0006918929,0.0004915506,0.0002871592,0.000132449,0.0002538057,0.0004013366,0.0009876173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004016255,"about_ca_system_score_gemma":0.0004796162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003001392,"about_ca_topic_score_gemma":0.008012415,"domain_scores_codex":[0.9995987,0.00003396557,0.00002961126,0.000173477,0.0001158631,0.00004853661],"domain_scores_gemma":[0.9995849,0.000145735,0.00005973906,0.00003970465,0.0001386573,0.00003132956],"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":[0.0001483168,0.00002045121,0.008059306,0.000139288,0.00002428602,0.00009696554,0.00008840625,0.0009330178,0.9776187,0.0001243437,0.0006264194,0.01212049],"study_design_scores_gemma":[0.0000386242,0.0003114109,0.2381657,0.0000491849,0.0001377076,0.0008305143,0.0002642825,0.06576836,0.6776891,0.0006924574,0.01600282,0.0000499564],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8696064,0.001327527,0.1017594,0.0003119405,0.0001155423,0.0004043478,0.02195096,0.001592807,0.002931042],"genre_scores_gemma":[0.737518,0.0005715537,0.2184957,0.0004037711,0.00006501519,0.0009631309,0.03674846,0.0005434178,0.004691099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003001392,"threshold_uncertainty_score":0.006069779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00834964555702429,"score_gpt":0.2679314533853114,"score_spread":0.2595818078282872,"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."}}