{"id":"W4393212108","doi":"10.1038/s41598-024-56786-9","title":"GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Convolutional neural network; Binary classification; Entropy (arrow of time); In silico; Machine learning; Deep learning; Support vector machine; Biology; Gene; Genetics","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":[0.000684521,0.0001347234,0.0001446238,0.0000907389,0.0001480906,0.0002393655,0.00009378692,0.000123895,0.00004797688],"category_scores_gemma":[0.0003839421,0.0001271721,0.00008287859,0.00008365894,0.0001450475,0.00001793443,0.00008090277,0.0001612735,0.000003333547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000138062,"about_ca_system_score_gemma":0.00006413874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001616052,"about_ca_topic_score_gemma":0.000009523643,"domain_scores_codex":[0.9986066,0.00003676466,0.0004618828,0.0004905651,0.0002272078,0.0001769781],"domain_scores_gemma":[0.9990743,0.00006694259,0.000256301,0.0003825982,0.0001610601,0.00005875535],"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":[0.00002919222,0.0000408032,0.009061132,0.000160059,0.00007839165,0.000008312679,0.0004783211,0.001909858,0.9772211,0.0003441161,0.005463592,0.005205132],"study_design_scores_gemma":[0.0004377498,0.0002156639,0.04165636,0.0002289221,0.0001135035,0.0001506495,0.0005702067,0.2275424,0.3750512,0.004137541,0.3492852,0.0006106225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8242739,0.0007984229,0.1727686,0.00008674867,0.001422729,0.0002056547,0.00004060509,0.0000280275,0.0003753013],"genre_scores_gemma":[0.975373,0.00006383505,0.02091177,0.00003252467,0.0001410606,0.00001413698,0.001763155,0.00002459463,0.001675887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6021699,"threshold_uncertainty_score":0.5185927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209636804286032,"score_gpt":0.2633443942355905,"score_spread":0.2512480261927302,"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."}}