{"id":"W4383501139","doi":"10.1101/2023.07.07.547075","title":"SPAT: Surface Protein Annotation Tool","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Institut de Valorisation des Données; Canada First Research Excellence Fund; Université de Montréal; Government of Canada; Génome Québec; Compute Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Annotation; In silico; Computer science; Computational biology; USable; Surface (topology); Surface protein; Gene; Artificial intelligence; Biology; Biochemistry; Mathematics; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002971629,0.0004017216,0.0002717483,0.00006981406,0.0001498718,0.00009923935,0.0003910256,0.0005962364,0.000004477328],"category_scores_gemma":[0.0001492114,0.0004523303,0.0001363824,0.0002586967,0.00009667082,0.000006411123,0.0005577822,0.0003754418,0.00006842021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006708828,"about_ca_system_score_gemma":0.0002581751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002467611,"about_ca_topic_score_gemma":0.000003010838,"domain_scores_codex":[0.9979842,0.000060922,0.000371888,0.0009840846,0.0002089727,0.0003899285],"domain_scores_gemma":[0.9978411,0.000009736701,0.0002989062,0.001372097,0.0003682065,0.0001099531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001606159,0.00004775269,0.0002799564,0.0000879997,0.00004163448,0.000007098341,9.571684e-7,0.0002033438,0.9976344,0.0003874042,0.001290199,0.000003174637],"study_design_scores_gemma":[0.0001632953,0.00005860652,0.005207138,0.000150257,0.00002908024,1.126195e-8,9.419084e-7,0.0002353365,0.9706976,0.00002330773,0.02283482,0.0005996322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612296,0.000248012,0.03566092,0.0004524033,0.0002838884,0.001211909,0.0003309333,0.0005756947,0.000006654245],"genre_scores_gemma":[0.9443782,0.0001937687,0.05437979,0.0001211276,0.0004380239,0.0002507692,0.000007227063,0.0001532102,0.00007787846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02693685,"threshold_uncertainty_score":0.9997929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577430963544602,"score_gpt":0.2481638858878699,"score_spread":0.2323895762524239,"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."}}