{"id":"W2518005235","doi":"10.1007/978-1-4939-3801-8_21","title":"Analysis of the Tumor Microenvironment Transcriptome via NanoString mRNA and miRNA Expression Profiling","year":2016,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Jewish General Hospital; McGill University","funders":"Fondation de l'Hôpital général juif; Conquer Cancer Foundation","keywords":"microRNA; Transcriptome; Profiling (computer programming); Gene expression profiling; Computational biology; Messenger RNA; Tumor microenvironment; Biology; Cancer research; Gene expression; Computer science; Gene; Tumor cells; Genetics","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":[0.0005532225,0.0001724808,0.0002879745,0.0001373386,0.00004950843,0.000003406309,0.0003095969,0.0002038026,0.00001399883],"category_scores_gemma":[0.00009179523,0.0001057253,0.0001944256,0.0002821643,0.0003119176,0.000001794073,0.0002407636,0.00009172597,3.322197e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001588067,"about_ca_system_score_gemma":0.00001772342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001268384,"about_ca_topic_score_gemma":0.000006931949,"domain_scores_codex":[0.9982373,0.0006153689,0.0003497355,0.0004988779,0.00005341859,0.0002452802],"domain_scores_gemma":[0.9991599,0.00003740601,0.0001544375,0.000580191,0.00002358379,0.00004443212],"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":[0.00002159727,0.00003448364,0.02247099,0.000006269446,0.0001132427,0.000001119619,0.00000968024,0.000008693934,0.9706321,0.0002898122,0.000003890369,0.006408109],"study_design_scores_gemma":[0.0002684761,0.00008656677,0.005269775,0.00001837303,0.0001071111,0.000008251208,0.000007765029,0.00003971267,0.9912568,0.0006173156,0.002180097,0.000139725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4512557,0.0006864783,0.5476196,0.0001440604,0.00002784479,0.000202402,0.0000177051,0.000005189811,0.00004104756],"genre_scores_gemma":[0.6961794,0.000203607,0.303185,0.0002488567,0.00001140345,0.00009722992,0.00002084931,0.00001620189,0.00003744171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2449237,"threshold_uncertainty_score":0.4311352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030015490663476,"score_gpt":0.3297397764833001,"score_spread":0.3194396215766653,"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."}}