{"id":"W2295742112","doi":"10.1007/978-3-319-24282-8_13","title":"Detecting Transmembrane Proteins Using Decision Trees","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Decision tree; Artificial intelligence; Domain (mathematical analysis); Machine learning; Support vector machine; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007426832,0.0003494573,0.000289381,0.000261885,0.0001531181,0.0001171102,0.0007785085,0.0003987233,0.00001511386],"category_scores_gemma":[0.0002753891,0.0003100818,0.00009503488,0.0001503547,0.0003274862,0.00001276425,0.0003760656,0.0005152993,0.000007882864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009309132,"about_ca_system_score_gemma":0.000380884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001639257,"about_ca_topic_score_gemma":0.000150869,"domain_scores_codex":[0.9980593,0.00002073064,0.0003865266,0.0006175565,0.0005451016,0.0003708102],"domain_scores_gemma":[0.9987988,0.00006067475,0.0002097628,0.0006369328,0.0001781161,0.000115721],"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":[0.00005329931,0.00001644498,0.0001199055,0.00007227669,0.00001735994,0.00001678878,0.0003420208,0.2902464,0.03353868,0.00007852064,0.00001135437,0.6754869],"study_design_scores_gemma":[0.000999066,0.0009288039,0.00008698561,0.0009576104,0.00003565215,0.0003448233,8.872402e-7,0.9113356,0.06019782,0.01093559,0.01274723,0.001429896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01066368,0.0004920454,0.9861366,0.00003824271,0.0004567,0.0003301048,0.000005139365,0.00002688301,0.001850594],"genre_scores_gemma":[0.4175786,0.00002782764,0.5810474,0.0002903192,0.0007225807,0.00000387506,0.00002199447,0.0000598894,0.0002475075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6740571,"threshold_uncertainty_score":0.9999352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037081816447624,"score_gpt":0.2776806096610663,"score_spread":0.2573097914965901,"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."}}