{"id":"W1529361704","doi":"10.1016/s0076-6879(09)67009-9","title":"Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures","year":2009,"lang":"en","type":"article","venue":"Methods in enzymology on CD-ROM/Methods in enzymology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Institut National Du Cancer; Institute of Cancer Research; Fondation pour la Recherche Médicale; Institut National de la Santé et de la Recherche Médicale","keywords":"Biology; DECIPHER; Stem cell; Computational biology; Transcriptome; Neural stem cell; Cellular differentiation; Scripting language; Adult stem cell; Computer science; Gene; Bioinformatics; Cell biology; Genetics; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001195838,0.0006859063,0.0008968974,0.001764852,0.0005392439,0.001153757,0.000444907,0.0004433188,0.001811678],"category_scores_gemma":[0.001568001,0.000471417,0.0008242473,0.002730841,0.0002877064,0.0009699645,0.001084345,0.0009111736,0.00108622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003579331,"about_ca_system_score_gemma":0.0009491841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001530684,"about_ca_topic_score_gemma":0.004037912,"domain_scores_codex":[0.9992979,0.0001343576,0.00006341814,0.0001978245,0.0002417006,0.00006482084],"domain_scores_gemma":[0.9984296,0.0005532922,0.0001326167,0.0004479077,0.0003475598,0.00008908834],"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":[0.0004650963,0.0002041931,0.01323548,0.0004029486,0.0004808368,0.000288341,0.0002325913,0.006790223,0.8034049,0.001543559,0.005607683,0.167344],"study_design_scores_gemma":[0.0001213503,0.0003789884,0.0873797,0.00009204509,0.0008445607,0.0007662097,0.0006591376,0.2515661,0.6096222,0.01340452,0.03503243,0.0001328845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3030869,0.001376145,0.6453834,0.0006242406,0.0002015997,0.0003166618,0.0260077,0.01944779,0.003555539],"genre_scores_gemma":[0.4487265,0.0005481275,0.4934821,0.0004076812,0.00009704119,0.0006280174,0.05141503,0.001619458,0.003075955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001811678,"threshold_uncertainty_score":0.006324232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06072368865616787,"score_gpt":0.4415077192062479,"score_spread":0.38078403055008,"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."}}