{"id":"W4416025381","doi":"10.1016/j.immuno.2025.100064","title":"DoggifAI: A transformer based approach for antibody caninisation","year":2025,"lang":"en","type":"article","venue":"ImmunoInformatics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Bristol; Royal Academy of Engineering; UK Research and Innovation; Israel Cancer Research Fund","keywords":"Transformer; Antibody; Human proteins; Sequence (biology); Sequence alignment","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003078088,0.0001371539,0.000257233,0.0001900493,0.0001657873,0.0000396856,0.0001161918,0.00008909178,0.0000590346],"category_scores_gemma":[0.0000675748,0.0001060032,0.0001670769,0.000244089,0.00007784556,0.0001298966,0.00001307939,0.0001629036,0.00002190302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005801893,"about_ca_system_score_gemma":0.0003586049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006558021,"about_ca_topic_score_gemma":0.000002240646,"domain_scores_codex":[0.9989392,0.0000136372,0.0003941743,0.00009809645,0.0002515592,0.0003033183],"domain_scores_gemma":[0.9993715,0.0001266402,0.00004942397,0.0002213899,0.0001658965,0.00006520991],"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":[0.02049985,0.004800392,0.05121461,0.04693464,0.003694847,0.00002863674,0.01294586,0.003656838,0.1586196,0.149137,0.08228409,0.4661836],"study_design_scores_gemma":[0.009626323,0.001030857,0.01921969,0.0004406971,0.0003375429,0.00004769125,0.001677335,0.6352941,0.03561171,0.000788528,0.2953888,0.0005366157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1000386,0.0004800618,0.6796212,0.002925517,0.0002516122,0.002426245,0.0001254035,0.0001294143,0.214002],"genre_scores_gemma":[0.8745436,0.0001276668,0.1041343,0.003353794,0.00009894469,0.0002137445,0.002002357,0.00002677531,0.01549881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.774505,"threshold_uncertainty_score":0.4322685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02170429463065195,"score_gpt":0.3646956850402162,"score_spread":0.3429913904095642,"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."}}