{"id":"W4393085018","doi":"10.1158/1538-7445.am2024-3536","title":"Abstract 3536: Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling","year":2024,"lang":"en","type":"article","venue":"Cancer Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Queen's University","funders":"","keywords":"Profiling (computer programming); Computational biology; Phenotype; Pipeline (software); Computer science; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005698799,0.001227507,0.0007808934,0.0008883203,0.0004090516,0.001408514,0.00198202,0.001106314,0.00996418],"category_scores_gemma":[0.001478998,0.0005996148,0.0007884593,0.0005523462,0.0004281719,0.0009002438,0.001564577,0.001661543,0.004811997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074652,"about_ca_system_score_gemma":0.001506596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0049413,"about_ca_topic_score_gemma":0.008363174,"domain_scores_codex":[0.9997163,0.00003020536,0.00001370916,0.0001045649,0.00009678683,0.00003841317],"domain_scores_gemma":[0.9996027,0.0001167117,0.00003782235,0.00008084982,0.0001098519,0.00005205412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001241417,0.000332962,0.01112007,0.0007817093,0.0004578284,0.001183785,0.0002589189,0.1230821,0.2462981,0.009479648,0.1935489,0.4122147],"study_design_scores_gemma":[0.00009459948,0.0001247061,0.002730101,0.00002577342,0.00004633993,0.0002166759,0.00004725084,0.8956671,0.06237505,0.009958478,0.02863469,0.00007927348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0416397,0.00110056,0.8107221,0.001135999,0.0002971122,0.0003625802,0.01675888,0.1232443,0.004738827],"genre_scores_gemma":[0.3621446,0.001213168,0.5563267,0.00171924,0.0001438068,0.001563278,0.04912147,0.006812525,0.02095524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00996418,"threshold_uncertainty_score":0.03333348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06110891044154527,"score_gpt":0.3501968021055509,"score_spread":0.2890878916640056,"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."}}