{"id":"W4409881650","doi":"10.1101/2025.04.25.650745","title":"SPAED: Harnessing AlphaFold Output for Accurate Segmentation of Phage Endolysin Domains","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Mitacs; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Vlaamse regering; Universiteit Gent; Fonds de Recherche du Québec - Santé; Fonds Wetenschappelijk Onderzoek","keywords":"Lysin; Segmentation; Bacteriophage; Computer science; Computational biology; Biology; Artificial intelligence; Genetics; Gene","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":[0.003298911,0.002208346,0.00151305,0.002864455,0.0009616397,0.002997995,0.002037979,0.001423779,0.0117038],"category_scores_gemma":[0.005665122,0.001075185,0.001890502,0.001398674,0.0006165248,0.001863974,0.002428989,0.001778861,0.009043988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008699239,"about_ca_system_score_gemma":0.001332752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00233648,"about_ca_topic_score_gemma":0.003307837,"domain_scores_codex":[0.9987181,0.000214473,0.00008948045,0.0005072189,0.0003570422,0.0001135622],"domain_scores_gemma":[0.9980068,0.0007403711,0.0001434095,0.0005376533,0.0004193474,0.0001524355],"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":[0.004245799,0.0005200361,0.02230708,0.002977913,0.001064921,0.00100682,0.001000643,0.08928242,0.1024844,0.008669791,0.3342568,0.4321834],"study_design_scores_gemma":[0.0003824782,0.0002138744,0.004958677,0.0001992392,0.0001008379,0.0003326737,0.0002208282,0.8290936,0.07395061,0.01679076,0.07361078,0.0001456998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.06414559,0.001078781,0.3540112,0.000508324,0.0004720252,0.0001783554,0.03300802,0.5417258,0.00487195],"genre_scores_gemma":[0.2423862,0.0007856884,0.5782394,0.0004162992,0.0001625422,0.0003957434,0.1183149,0.05481575,0.004483486],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0117038,"threshold_uncertainty_score":0.0391531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603446971507516,"score_gpt":0.2512979664942107,"score_spread":0.2352634967791356,"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."}}