{"id":"W2884030511","doi":"10.1371/journal.pcbi.1006384","title":"Context-explorer: Analysis of spatially organized protein expression in high-throughput screens","year":2019,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; University of Toronto","funders":"","keywords":"Context (archaeology); Biology; Computer science; Population; Software; Computational biology; SOX2; Spatial contextual awareness; Transcription factor; Artificial intelligence; Genetics; Operating system","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":[0.001572147,0.001342871,0.001339827,0.001909573,0.0005105552,0.001355097,0.000990927,0.0007215456,0.003340329],"category_scores_gemma":[0.001875669,0.0004916565,0.001085654,0.001038768,0.0002697702,0.0005990632,0.001095565,0.0007004871,0.001409465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003736652,"about_ca_system_score_gemma":0.0005652059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001077199,"about_ca_topic_score_gemma":0.00242116,"domain_scores_codex":[0.9990308,0.0001968919,0.0000763476,0.0001859382,0.0004109387,0.00009899222],"domain_scores_gemma":[0.9988087,0.0007204941,0.000140823,0.0001475099,0.00009754683,0.0000848697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001321303,0.0003260934,0.0150414,0.001242581,0.0004815825,0.001026879,0.0003539876,0.01006674,0.8713654,0.00304964,0.01246685,0.08325766],"study_design_scores_gemma":[0.0003302693,0.0008047168,0.04618638,0.0001549999,0.0003515625,0.001708694,0.0002836717,0.207566,0.6973001,0.003677638,0.0413305,0.0003054503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4449702,0.002140219,0.4017386,0.0004491585,0.0001101232,0.0008447919,0.04377297,0.09974384,0.006230026],"genre_scores_gemma":[0.4654245,0.001690309,0.498654,0.0003070107,0.00003438208,0.00359655,0.02096,0.004888424,0.004444795],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003340329,"threshold_uncertainty_score":0.0111745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009231491485339516,"score_gpt":0.2518109658304648,"score_spread":0.2425794743451253,"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."}}