{"id":"W4405860078","doi":"10.5376/cmb.2024.14.0013","title":"AI in Biology: Transforming Genomic Research with Machine Learning","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biology; Computational biology; Cognitive science; Evolutionary biology; Data science; Computer science; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009249844,0.0001836412,0.0001871933,0.0004125068,0.0001094581,0.00005712626,0.0003032403,0.0002737301,0.00002578279],"category_scores_gemma":[0.0001481119,0.0001484373,0.00007634675,0.0003975708,0.0005143481,0.00000545056,0.0001697737,0.000595563,0.00005901897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005298788,"about_ca_system_score_gemma":0.0003874437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005241041,"about_ca_topic_score_gemma":0.0000600204,"domain_scores_codex":[0.9980906,0.000296773,0.0003268422,0.0004985163,0.000216326,0.0005709504],"domain_scores_gemma":[0.9993858,0.000108629,0.00002551662,0.0001671921,0.0001811092,0.0001317791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002421078,0.0001087116,0.005525977,0.0001804537,0.0002041094,0.00009575883,0.0002560099,0.005151339,0.9079066,0.0259168,0.0003368433,0.05407529],"study_design_scores_gemma":[0.005510997,0.0111022,0.007920895,0.000454123,0.00006651861,0.0005424064,0.0005186819,0.2308875,0.07526398,0.2829804,0.3825275,0.002224761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6087426,0.006186434,0.3800506,0.002729238,0.0001963481,0.0004917118,0.00004850299,0.0000460651,0.001508518],"genre_scores_gemma":[0.9935738,0.0002119853,0.004338314,0.0004586011,0.0001212802,0.00003895511,0.001082011,0.00002969624,0.0001453562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326426,"threshold_uncertainty_score":0.6053098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516239825888492,"score_gpt":0.3662489068911247,"score_spread":0.3410865086322398,"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."}}