{"id":"W3020323696","doi":"10.1186/s12859-019-3311-6","title":"TooT-T: discrimination of transport proteins from non-transport proteins","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University; Saudi Arabian Cultural Bureau; Genome Canada","keywords":"Computational biology; Classifier (UML); Membrane protein; Membrane transport protein; Protein function prediction; Computer science; Function (biology); Protein sequencing; Transport protein; Protein function; Artificial intelligence; Machine learning; Membrane; Bioinformatics; Chemistry; Biology; Peptide sequence; Biochemistry; Genetics; 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":[0.00155231,0.001293552,0.001146664,0.002210376,0.0007154442,0.001273689,0.001280364,0.001762113,0.001596717],"category_scores_gemma":[0.002619116,0.0001755305,0.001906671,0.001062051,0.0002688294,0.001104141,0.0009934509,0.001404024,0.001471544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006207577,"about_ca_system_score_gemma":0.001099491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00327161,"about_ca_topic_score_gemma":0.002931527,"domain_scores_codex":[0.9987653,0.0001953515,0.00009673036,0.0003783861,0.0003780547,0.0001861772],"domain_scores_gemma":[0.9983145,0.0005929554,0.0001632847,0.0001502124,0.0005785058,0.0002005702],"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":[0.001588939,0.001064283,0.06729449,0.0005062704,0.0007440182,0.0009411369,0.0001438998,0.1040848,0.06538628,0.002359789,0.03358662,0.7222995],"study_design_scores_gemma":[0.00003139102,0.000537332,0.007947557,0.0000430706,0.0001714782,0.0007708433,0.00007720988,0.9653769,0.01868763,0.002250536,0.004057639,0.00004836678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5816207,0.004609271,0.3858778,0.001290636,0.001119881,0.0005415518,0.004491885,0.008968371,0.01147985],"genre_scores_gemma":[0.891838,0.0007766847,0.09370402,0.0003871433,0.0003017845,0.0002239492,0.008233196,0.0002143355,0.004320861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00327161,"threshold_uncertainty_score":0.008209527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01412632739812552,"score_gpt":0.2362567035197294,"score_spread":0.2221303761216039,"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."}}