{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004873072,0.002654603,0.002892554,0.003933475,0.001426117,0.003839754,0.004458409,0.001892383,0.01993411],"category_scores_gemma":[0.008738207,0.00176892,0.002360617,0.004295668,0.0006645306,0.00236734,0.003891322,0.002333954,0.02286454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007945649,"about_ca_system_score_gemma":0.003233502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833919,"about_ca_topic_score_gemma":0.00374116,"domain_scores_codex":[0.9977115,0.000447905,0.00032317,0.0008399645,0.0005071169,0.0001703038],"domain_scores_gemma":[0.9972115,0.001401912,0.0003532307,0.0004608838,0.0003717013,0.0002007981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002388161,0.00015221,0.008701332,0.01175078,0.001615263,0.001437923,0.001204606,0.007696854,0.08657169,0.01218928,0.7660329,0.100259],"study_design_scores_gemma":[0.0009399042,0.0003872549,0.009367557,0.000922442,0.0008395347,0.002784283,0.0005648886,0.05877905,0.08367179,0.02832717,0.812884,0.0005321298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01016325,0.003807274,0.2209683,0.0008344079,0.0004242434,0.0005185495,0.4262372,0.3301683,0.006878485],"genre_scores_gemma":[0.03123018,0.001910178,0.3261774,0.001145606,0.0001166923,0.002709501,0.597129,0.03476281,0.004818724],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01993411,"threshold_uncertainty_score":0.06668627,"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."}}