{"id":"W4295049068","doi":"10.1371/journal.pone.0272302","title":"TMExplorer: A tumour microenvironment single-cell RNAseq database and search tool","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; Ontario Institute for Cancer Research; Children’s Health Research Institute; SickKids Foundation; Western University","funders":"Schulich School of Medicine and Dentistry; Government of Canada; Ontario Institute for Cancer Research; Lawson Health Research Institute; Natural Sciences and Engineering Research Council of Canada; Children's Health Research Institute","keywords":"Metadata; Computer science; Tumor microenvironment; Stromal cell; Database; Interface (matter); Computational biology; Cancer; Biology; World Wide Web; Cancer research","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522403,0.0001413573,0.0001425703,0.00003593629,0.0002284657,0.00002656572,0.0001747352,0.00004060312,0.0001448172],"category_scores_gemma":[0.0000128771,0.0001567856,0.00004508425,0.00005094341,0.0000791259,0.000004151535,0.000318257,0.0001708221,0.00001331762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003274424,"about_ca_system_score_gemma":0.00003092677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002879489,"about_ca_topic_score_gemma":0.000007842375,"domain_scores_codex":[0.9988418,0.00008021978,0.0001609194,0.0003907823,0.0002638763,0.0002624575],"domain_scores_gemma":[0.999544,0.000008279785,0.0000292186,0.0003124162,0.00001633981,0.00008974688],"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":[0.0001402029,0.002247448,0.002450344,0.00005997818,0.0000610583,0.00001549571,0.0001005789,0.00002796773,0.9942876,0.000005830551,0.0003008635,0.0003026623],"study_design_scores_gemma":[0.0008801759,0.0006909891,0.0002867075,0.0000132656,0.0000575887,0.000009661614,0.0001430048,0.0002219036,0.9932129,0.000007277805,0.004254869,0.0002216697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976451,0.001209589,0.0001786469,0.0002006226,0.00003751577,0.000239749,0.0001896316,0.00001431604,0.0002848348],"genre_scores_gemma":[0.9951575,0.0003886845,0.002403694,0.0004605496,0.0001202548,0.00005419368,0.0004252529,0.00003653037,0.0009533411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003954005,"threshold_uncertainty_score":0.6393532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288678297428299,"score_gpt":0.2005376985272143,"score_spread":0.1576509155529313,"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."}}