{"id":"W3160351040","doi":"10.1038/s42003-021-02066-5","title":"CellectSeq: In silico discovery of antibodies targeting integral membrane proteins combining in situ selections and next-generation sequencing","year":2021,"lang":"en","type":"article","venue":"Communications Biology","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; University of Toronto","funders":"Sickkids Research Institute; Scheme for Promotion of Academic and Research Collaboration; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Computational biology; In silico; Integral membrane protein; Transmembrane protein; Biology; Tetraspanin; Phage display; Membrane protein; In situ; DNA sequencing; Antibody; Biochemistry; Chemistry; Membrane; Cell; Genetics; DNA; 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":[0.002251398,0.001270422,0.001052159,0.0008286806,0.0004511489,0.001409715,0.0009062142,0.0007251343,0.001793958],"category_scores_gemma":[0.00196587,0.0005274133,0.0008889706,0.0004915131,0.0003003293,0.0004823665,0.0005642731,0.0008462617,0.0009369695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005488158,"about_ca_system_score_gemma":0.001098409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009749268,"about_ca_topic_score_gemma":0.002016185,"domain_scores_codex":[0.9992731,0.0001494477,0.00005857416,0.0002180973,0.0002335999,0.00006712935],"domain_scores_gemma":[0.9993147,0.0004052496,0.00008184108,0.00004480814,0.0001039913,0.00004949419],"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":[0.0008927617,0.0002207699,0.01260241,0.0008676075,0.0005836499,0.0005081964,0.0002213568,0.03686894,0.8793564,0.003999541,0.006104594,0.05777373],"study_design_scores_gemma":[0.0001405481,0.0004295012,0.004732152,0.00003345691,0.0002323216,0.0004504372,0.00007319497,0.4806984,0.4928577,0.001803083,0.01845803,0.00009117344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3191495,0.001056973,0.6537709,0.0002876155,0.0001920066,0.0004900778,0.006817813,0.01489987,0.003335174],"genre_scores_gemma":[0.2762168,0.0006180208,0.7082135,0.0003812306,0.00004171209,0.0006774723,0.01043581,0.001358725,0.002056691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002251398,"threshold_uncertainty_score":0.01190668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.119483040984937,"score_gpt":0.3520724922583462,"score_spread":0.2325894512734092,"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."}}