{"id":"W2981825750","doi":"10.1016/j.coph.2019.09.004","title":"Unravelling fibrosis using single-cell transcriptomics","year":2019,"lang":"en","type":"review","venue":"Current Opinion in Pharmacology","topic":"Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; University of Edinburgh; Medical Research Council Canada; Wellcome Trust","keywords":"Fibrosis; Druggability; Context (archaeology); Transcriptome; Identification (biology); Computational biology; Disease; Cell; Extracellular matrix; Bioinformatics; Biology; Medicine; Pathology; Cell biology; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001931434,0.0006233759,0.002198645,0.0006144581,0.00006130939,0.00002576188,0.0002838546,0.0004918419,0.0003936997],"category_scores_gemma":[0.000009915932,0.000585623,0.001004261,0.0004560262,0.0001038061,0.00009635476,0.00009734491,0.0011123,0.0001642137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007274239,"about_ca_system_score_gemma":0.0007867205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008833231,"about_ca_topic_score_gemma":6.889218e-8,"domain_scores_codex":[0.9969791,0.0002751859,0.001105051,0.0007689301,0.0002436023,0.0006281973],"domain_scores_gemma":[0.9988359,0.0001430962,0.0003984214,0.0002974338,0.00008216405,0.0002429442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001115056,0.002320731,0.00002839498,0.3755143,0.0002799413,0.0000336759,0.0001415539,0.00005312126,0.0009198257,0.00007940505,0.009534648,0.6099793],"study_design_scores_gemma":[0.00120689,0.0006919765,6.728749e-7,0.03874386,0.00214405,0.000098181,0.00001252582,0.0009822882,0.00001990762,0.000006692896,0.955605,0.000487921],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004409249,0.9673402,0.0002686534,0.00002707068,0.03003504,0.001716443,0.0002493686,0.00005417114,0.0002649405],"genre_scores_gemma":[0.000114084,0.9961478,0.0001578761,0.00004854925,0.002511156,0.00008709736,0.0007207393,0.0001173375,0.00009533345],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9460704,"threshold_uncertainty_score":0.9996595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.200603512415241,"score_gpt":0.4318735675780548,"score_spread":0.2312700551628139,"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."}}