{"id":"W3001208427","doi":"10.1083/jcb.201904090","title":"A reference library for assigning protein subcellular localizations by image-based machine learning","year":2020,"lang":"en","type":"article","venue":"The Journal of Cell Biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Hospital","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Endoplasmic reticulum; Subcellular localization; Biology; Protein subcellular localization prediction; Organelle; Protein Sorting Signals; Cell biology; Fusion protein; Protein targeting; Mutant; Cell; Computational biology; Cytoplasm; Biochemistry; Membrane protein; Peptide sequence; Gene; Signal peptide; Recombinant DNA","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":[0.002480618,0.003551785,0.002093421,0.009822178,0.001553674,0.0021741,0.005317302,0.002178024,0.04188036],"category_scores_gemma":[0.008703576,0.001827983,0.0016417,0.0108414,0.0005063076,0.001904238,0.002162088,0.002332312,0.05259925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168223,"about_ca_system_score_gemma":0.002820835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004335592,"about_ca_topic_score_gemma":0.007326232,"domain_scores_codex":[0.9981736,0.0002539089,0.000256971,0.0004448252,0.0007615492,0.0001090467],"domain_scores_gemma":[0.9946167,0.001441015,0.0003449506,0.001786165,0.001604541,0.0002066228],"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":[0.0004237285,0.0004642589,0.001326874,0.004852022,0.0004183516,0.0007095318,0.0001497398,0.01778577,0.04950497,0.01001776,0.3227622,0.5915849],"study_design_scores_gemma":[0.0003429075,0.0005456802,0.005581255,0.001350537,0.0003692287,0.002144037,0.00009197739,0.1019425,0.1159115,0.02718526,0.7441983,0.000336827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003851893,0.002421552,0.7698922,0.0002419759,0.00027872,0.0004165805,0.07600349,0.1344726,0.01242098],"genre_scores_gemma":[0.01284339,0.003278215,0.6578525,0.0002985508,0.0001030268,0.002198718,0.2916576,0.01622034,0.01554761],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04188036,"threshold_uncertainty_score":0.1401038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076829043483213,"score_gpt":0.2402807207006582,"score_spread":0.229512430265826,"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."}}