{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006778683,0.00131478,0.0007242544,0.001339393,0.0004607971,0.001077795,0.002016634,0.001730388,0.007195593],"category_scores_gemma":[0.001994939,0.0005168523,0.00209791,0.0008521504,0.0005321829,0.001022042,0.0008834503,0.001848898,0.003231981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556266,"about_ca_system_score_gemma":0.001331121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01025559,"about_ca_topic_score_gemma":0.02735763,"domain_scores_codex":[0.999653,0.00005872358,0.00001700767,0.0001569549,0.00006683407,0.0000473954],"domain_scores_gemma":[0.9996145,0.0001959357,0.00003143021,0.00006968948,0.00005794711,0.00003044549],"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":[0.0009096241,0.0003910042,0.005035165,0.0008688597,0.0004106348,0.000593144,0.0002027727,0.3712357,0.03918483,0.02376653,0.08402888,0.4733729],"study_design_scores_gemma":[0.0000690204,0.0001362733,0.0004716608,0.00002768168,0.0000434152,0.0002064995,0.00002712592,0.9593399,0.005055951,0.01577296,0.01883125,0.0000182821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05672435,0.003789396,0.8724489,0.001688829,0.0005132435,0.000451257,0.01327549,0.03735071,0.01375775],"genre_scores_gemma":[0.3319824,0.001743956,0.6088473,0.002526813,0.0002135743,0.0007391344,0.03500348,0.002236938,0.0167063],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01025559,"threshold_uncertainty_score":0.02407163,"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."}}