{"id":"W3034105569","doi":"10.1136/jitc-2019-000266","title":"Nectin4 is a novel TIGIT ligand which combines checkpoint inhibition and tumor specificity","year":2020,"lang":"en","type":"article","venue":"Journal for ImmunoTherapy of Cancer","topic":"Immune Cell Function and Interaction","field":"Immunology and Microbiology","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Israel Science Foundation; Hebrew University of Jerusalem; Centre in Green Chemistry and Catalysis; Israel Cancer Research Fund","keywords":"TIGIT; Cancer research; Medicine; Computational biology; Computer science; Internal medicine; Biology; Immunotherapy; Cancer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001411746,0.0004896099,0.0002553629,0.0002449576,0.0001997752,0.0003145492,0.0004451576,0.0005762527,0.001294115],"category_scores_gemma":[0.0001307132,0.0001023538,0.0002672366,0.0001829719,0.0002070051,0.0002796607,0.0003443698,0.0004782847,0.0004008645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007537367,"about_ca_system_score_gemma":0.0003172498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004018714,"about_ca_topic_score_gemma":0.0006561023,"domain_scores_codex":[0.9998764,0.00002593747,0.000009098608,0.00002767652,0.00003041414,0.00003054217],"domain_scores_gemma":[0.9999435,0.000006871097,0.00001379764,0.000005418147,0.000009557421,0.00002079331],"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":[0.00009476604,0.00005377868,0.0004774363,0.00007958236,0.00001625592,0.0001806688,0.0000104912,0.0004077471,0.9904517,0.0003805124,0.0003012839,0.00754573],"study_design_scores_gemma":[0.00008711845,0.001155364,0.002799146,0.00002482104,0.00006134244,0.004408065,0.00002176796,0.002745293,0.9613219,0.000237297,0.02712096,0.00001695717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436474,0.02334405,0.0196317,0.0009278712,0.0001932501,0.000181974,0.0004138202,0.0003823395,0.01127757],"genre_scores_gemma":[0.9661483,0.005003535,0.02089062,0.0004711251,0.00006871182,0.0001122523,0.001054438,0.0000333241,0.006217562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001294115,"threshold_uncertainty_score":0.005468726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02615766639445402,"score_gpt":0.2801342962558834,"score_spread":0.2539766298614293,"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."}}