{"id":"W2318484973","doi":"10.1101/046474","title":"Probabilistic estimation of short sequence expression using RNA-Seq data and the “positional bootstrap”","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ontario Genomics Institute; Ontario Genomics","keywords":"RNA-Seq; Sequence (biology); Probabilistic logic; Computer science; RNA splicing; Expression (computer science); Alternative splicing; Algorithm; Computational biology; RNA; Data mining; Transcriptome; Biology; Gene expression; Gene; Genetics; Artificial intelligence; Messenger RNA","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009408335,0.0008149577,0.001059695,0.001775908,0.0006102685,0.001576362,0.00181643,0.001166406,0.001320115],"category_scores_gemma":[0.02891518,0.0007493997,0.001342148,0.001902344,0.00181158,0.001382253,0.001487437,0.00192583,0.0007871459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009465222,"about_ca_system_score_gemma":0.0007937004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001924661,"about_ca_topic_score_gemma":0.001916644,"domain_scores_codex":[0.9958479,0.001907814,0.0001820209,0.0009429297,0.0009861442,0.0001330897],"domain_scores_gemma":[0.9826644,0.0131227,0.001426193,0.001375781,0.00119137,0.000219581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007373774,0.00015317,0.03933696,0.001173231,0.0009751449,0.0005099862,0.0005427752,0.6470986,0.0830814,0.07261707,0.005330763,0.1484436],"study_design_scores_gemma":[0.00001846654,0.00003816298,0.004269729,0.00005463217,0.00003520341,0.00009048769,0.00003349319,0.9370567,0.01224766,0.04355916,0.002550837,0.0000454887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02349252,0.0003019279,0.9739684,0.0001424194,0.00003247095,0.00004855157,0.0005786818,0.0009996149,0.0004353465],"genre_scores_gemma":[0.502717,0.0005189435,0.4876108,0.0004880789,0.0001409524,0.0004194003,0.006383626,0.0007914728,0.0009296983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009408335,"threshold_uncertainty_score":0.04975665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0441296421959366,"score_gpt":0.2980396330273567,"score_spread":0.2539099908314201,"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."}}