{"id":"W4400554183","doi":"10.1038/s41467-024-49710-2","title":"High throughput platform technology for rapid target identification in personalized phage therapy","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McMaster University","keywords":"Phage therapy; Escherichia coli; Salmonella enterica; Biofilm; Microbiology; Bacteriophage; Salmonella; Luciferase; Computational biology; Staphylococcus aureus; Chemistry; Biology; Bacteria; Gene; Biochemistry; Genetics","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":[0.000194642,0.0001071231,0.0001116378,0.0001201386,0.000217067,0.00007805763,0.0006979743,0.0002437262,0.0009065617],"category_scores_gemma":[0.0000395305,0.00009844056,0.00007728601,0.0005634933,0.0002099916,0.0003619592,0.0001792511,0.0006092545,0.0001910122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001847552,"about_ca_system_score_gemma":0.00001691677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008736397,"about_ca_topic_score_gemma":0.0004972433,"domain_scores_codex":[0.9992806,0.0000276875,0.0002203321,0.0002390642,0.00006741931,0.0001648651],"domain_scores_gemma":[0.9989836,0.0001387387,0.00005074079,0.000786547,0.00001729671,0.00002311128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004499067,0.0004064915,0.0004484724,0.00001795471,0.00006950158,0.000002337267,0.001506429,0.00003283065,0.8172637,0.06749713,0.03014664,0.08256354],"study_design_scores_gemma":[0.0003747127,0.00003380688,0.007701305,0.00002861108,0.00001174109,0.00001284088,0.0001860865,0.001181714,0.01317363,0.005201911,0.9719354,0.000158259],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7405226,0.08699769,0.01310564,0.1306751,0.004420561,0.005306567,0.0009875529,0.001496039,0.01648821],"genre_scores_gemma":[0.9814259,0.002169977,0.0143696,0.0003343421,0.00003170707,0.0002783023,0.000243723,0.00001970995,0.001126698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9417887,"threshold_uncertainty_score":0.9926215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067746802543232,"score_gpt":0.3124836638387269,"score_spread":0.2918061958132946,"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."}}