{"id":"W4306800518","doi":"10.1007/978-3-031-17024-9_1","title":"TooT-BERT-T: A BERT Approach on Discriminating Transport Proteins from Non-transport Proteins","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Transmembrane protein; Transporter; Transport protein; Classifier (UML); Representation (politics); Membrane transport protein; Membrane; Membrane protein; Chemistry; Computer science; Computational biology; Artificial intelligence; Biology; Biochemistry; Gene","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":[0.00100331,0.002507816,0.001953151,0.002166755,0.0008896866,0.002224377,0.002767135,0.00282977,0.01365857],"category_scores_gemma":[0.002081172,0.0007917729,0.001886136,0.002057789,0.0009162704,0.002205096,0.002044382,0.002680292,0.007208299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007659241,"about_ca_system_score_gemma":0.0009559882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002089142,"about_ca_topic_score_gemma":0.003019626,"domain_scores_codex":[0.9994023,0.0001030183,0.00002517811,0.0001208131,0.0002694274,0.00007934056],"domain_scores_gemma":[0.9991724,0.0003536115,0.00004062302,0.0001245321,0.0001806453,0.0001281787],"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":[0.0009393998,0.0002773569,0.001163796,0.0006206508,0.0002195205,0.0006778763,0.00008844168,0.07657644,0.07390559,0.1050726,0.05653642,0.6839219],"study_design_scores_gemma":[0.00004080026,0.0001646279,0.0007203597,0.00003740744,0.0000693697,0.0006204165,0.00003469608,0.8359194,0.02150264,0.1087764,0.03201988,0.00009390742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005559201,0.001123275,0.9773032,0.0005455784,0.0006507297,0.00008904048,0.0006955134,0.00383425,0.01019915],"genre_scores_gemma":[0.1017511,0.001634841,0.8285244,0.0009625941,0.0008064499,0.0002657497,0.003016687,0.002814015,0.06022418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01365857,"threshold_uncertainty_score":0.0456925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009424661949098445,"score_gpt":0.2156757947777839,"score_spread":0.2062511328286855,"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."}}