{"id":"W3118393359","doi":"10.1101/2020.12.31.425022","title":"Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Transcriptome; Reusability; Snapshot (computer storage); Computer science; Data sharing; RNA-Seq; Computational biology; Data mining; Data science; Biology; Database; Gene; Genetics; Software; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00101589,0.0002944151,0.0002598432,0.00006303651,0.0001051128,0.0002974012,0.0006788013,0.000434648,0.00001660578],"category_scores_gemma":[0.0002231053,0.0002814078,0.00006096553,0.0002167681,0.00007573199,0.00002683752,0.0002745057,0.0003705925,0.000002339739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284337,"about_ca_system_score_gemma":0.0005275031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002795076,"about_ca_topic_score_gemma":0.0002233893,"domain_scores_codex":[0.9972711,0.0004367754,0.0004322584,0.001420548,0.0002068029,0.0002325705],"domain_scores_gemma":[0.9970304,0.00001877167,0.0001285892,0.002508923,0.0002015562,0.0001117032],"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":[0.00003438946,0.00006315148,0.00095677,0.00006676112,0.00002355791,0.00000218364,0.0001314829,0.00008311067,0.9982032,0.000004219467,0.0004131401,0.00001804565],"study_design_scores_gemma":[0.0003463892,0.00003655453,0.03646346,0.0001118166,0.00005960742,2.478964e-8,0.000138873,0.001162847,0.9560146,0.000001697869,0.005221903,0.0004421829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842265,0.001657504,0.01153893,0.001038719,0.000425903,0.000628821,0.0004584614,0.00002288251,0.000002228693],"genre_scores_gemma":[0.9920639,0.0004767024,0.005846178,0.0009235588,0.0004585485,0.000136308,0.00005919207,0.0000347599,8.993258e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04218854,"threshold_uncertainty_score":0.9999638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0658469748120224,"score_gpt":0.2556697400279115,"score_spread":0.1898227652158891,"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."}}