{"id":"W2141474097","doi":"10.1109/iscas.2008.4541818","title":"Prediction of protein-coding regions in DNA sequences using a model-based approach","year":2008,"lang":"en","type":"article","venue":"","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Exon; Coding region; Intron; Computer science; Algorithm; Coding (social sciences); Computational biology; DNA sequencing; Pattern recognition (psychology); DNA; Artificial intelligence; Mathematics; Genetics; Gene; Biology; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00008169487,0.0000776772,0.0001088206,0.00007616166,0.00005086283,0.000003494741,0.00008193051,0.00008553956,0.000003952387],"category_scores_gemma":[0.00002311954,0.00006658518,0.00007305049,0.0001800039,0.0001030045,0.000006588993,0.00002015913,0.00004333788,3.55702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001488577,"about_ca_system_score_gemma":0.0001198842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002212072,"about_ca_topic_score_gemma":0.00002362588,"domain_scores_codex":[0.9993805,0.00002376917,0.0001759791,0.0002024815,0.00009580117,0.0001215275],"domain_scores_gemma":[0.9997009,0.00000214176,0.00006096044,0.0001600309,0.00004553181,0.00003045183],"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":[0.00001898982,0.00006099815,0.003202535,0.00001540076,0.00001119123,0.00000186753,0.00002918293,0.05478077,0.9417124,0.0001144841,0.00001477907,0.00003737314],"study_design_scores_gemma":[0.0002119413,0.00006825422,0.0002118963,0.00001888644,0.00001144302,0.00001029657,0.00008108456,0.4686317,0.5304872,0.0001556714,0.00002413521,0.00008748598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8655629,0.0000801319,0.132607,0.00002842643,0.000005718455,0.0001062902,0.000005760732,0.000006797449,0.00159692],"genre_scores_gemma":[0.9883395,0.00002400195,0.01129463,0.00004247725,0.00002367099,0.00001577579,0.00004345537,0.000005826659,0.00021059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4138509,"threshold_uncertainty_score":0.2715265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05789175293208839,"score_gpt":0.2489189611319748,"score_spread":0.1910272081998864,"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."}}