{"id":"W4399151368","doi":"10.48550/arxiv.2405.17631","title":"BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wu Tsai Neurosciences Institute, Stanford University; Genentech; Defense Advanced Research Projects Agency; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Computer science; Perturbation (astronomy); Artificial intelligence; Physics; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001107806,0.0002354383,0.0001616799,0.0001886987,0.0002554046,0.0002466126,0.001027507,0.0001589377,0.00001227051],"category_scores_gemma":[0.000006068421,0.0002712786,0.0001697405,0.0003273753,0.00004771915,0.0003177164,0.001057877,0.0002394399,0.0000778484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002448599,"about_ca_system_score_gemma":0.0002006711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003975649,"about_ca_topic_score_gemma":0.000003335706,"domain_scores_codex":[0.998347,0.00005555749,0.0001730536,0.001086445,0.00008128237,0.0002566852],"domain_scores_gemma":[0.9987922,0.00003900686,0.0001094641,0.0008321086,0.0001091726,0.0001179905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001823443,0.0003809127,0.000170641,0.0002049062,0.0001502721,0.00008260558,0.001330957,0.3981742,0.001113671,0.5926123,0.002307556,0.003453796],"study_design_scores_gemma":[0.000173288,0.00006118444,0.000230133,0.00004070258,0.00005216419,0.000002623992,0.00006352302,0.8278976,0.0005707216,0.1695511,0.001065707,0.0002912525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05170648,0.0003538917,0.9460233,0.0002283716,0.0005488853,0.0005841188,0.00004513132,0.0002334899,0.0002762639],"genre_scores_gemma":[0.9513658,0.00007190412,0.04604825,0.0001769661,0.0001527391,0.00002540755,0.00007672844,0.00002019203,0.002061999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8999751,"threshold_uncertainty_score":0.999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09494131121400051,"score_gpt":0.2310760300847567,"score_spread":0.1361347188707561,"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."}}