{"id":"W1764787399","doi":"10.1186/s12864-015-1671-5","title":"Gene expression analysis of skin grafts and cultured keratinocytes using synthetic RNA normalization reveals insights into differentiation and growth control","year":2015,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Wound Healing and Treatments","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tekes; Uppsala Multidisciplinary Center for Advanced Computational Science; Sigrid Juséliuksen Säätiö; Samfundet Folkhälsan; Science for Life Laboratory; Vetenskapsrådet; Institute of Genetics; Center for Innovative Medicine; Instrumentariumin Tiedesäätiö; Karolinska Institutet; Helsingin ja Uudenmaan Sairaanhoitopiiri; Helsingin Yliopisto","keywords":"Biology; RNA; HaCaT; Transcriptome; Gene expression; Cell culture; Molecular biology; Cell biology; Gene; Cellular differentiation; Gene expression profiling; Keratinocyte; Cell; Cell type; Messenger RNA; Epidermis (zoology); Genetics; Anatomy","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":[0.0005027302,0.0005645303,0.0004193558,0.0005151562,0.0003098906,0.0006324349,0.000282193,0.0003151279,0.001084094],"category_scores_gemma":[0.000439299,0.0002001857,0.0006628863,0.0005285252,0.0005166905,0.0002333927,0.0002676241,0.0008062623,0.0007665159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003870154,"about_ca_system_score_gemma":0.0003719727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000385333,"about_ca_topic_score_gemma":0.0008047601,"domain_scores_codex":[0.9993771,0.00006862802,0.00005416972,0.0002557475,0.0001853966,0.00005902687],"domain_scores_gemma":[0.9996573,0.00009451854,0.00007661348,0.00005533027,0.0000923335,0.00002385188],"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":[0.00003883211,0.000007983367,0.0002396774,0.00004455623,0.000004715027,0.00001740232,0.00002132879,0.000096763,0.998151,0.00005102384,0.00001885657,0.001307813],"study_design_scores_gemma":[0.000002391833,0.0001010683,0.005394643,0.00001120716,0.0000236533,0.00009459877,0.0000424401,0.001689579,0.9905599,0.00008464274,0.001986186,0.000009803101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8848623,0.002399946,0.09855879,0.0001546105,0.0002403688,0.0002319426,0.007738505,0.0009833049,0.004830253],"genre_scores_gemma":[0.8718763,0.002451917,0.1037752,0.0003046271,0.0000652204,0.0007167744,0.01494218,0.0008998361,0.004967924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001084094,"threshold_uncertainty_score":0.003626645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02771377469817113,"score_gpt":0.2720336296515332,"score_spread":0.2443198549533621,"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."}}