{"id":"W4405247314","doi":"10.1021/acs.biochem.4c00659","title":"Discovery of Cryptic Natural Products Using High-Throughput Elicitor Screening on Agar Media","year":2024,"lang":"en","type":"article","venue":"Biochemistry","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Drug Research and Development","funders":"National Institutes of Health; National Research Foundation of Korea; National Institute of General Medical Sciences; Korea Basic Science Institute; National Research Foundation","keywords":"Natural product; Elicitor; Biology; Agar; Computational biology; High-throughput screening; Genome; Agar plate; Gene; Microbiology; Bacteria; Biochemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002405149,0.0005010859,0.0003683086,0.0003879642,0.0001454192,0.000443199,0.0002275448,0.0002334326,0.0007041066],"category_scores_gemma":[0.0002490161,0.0001005875,0.0002659979,0.0004659436,0.0001344675,0.0002807064,0.0004578636,0.0004243054,0.0003882247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183822,"about_ca_system_score_gemma":0.0001430048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000257336,"about_ca_topic_score_gemma":0.0006511953,"domain_scores_codex":[0.9997875,0.00005092767,0.00001941939,0.00003496618,0.00007653114,0.0000306222],"domain_scores_gemma":[0.9998503,0.00005267378,0.00003395433,0.00001844464,0.00002172039,0.00002289873],"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":[0.00005851635,0.00002351851,0.0002713963,0.0000350969,0.000004954233,0.00005952684,0.00001013089,0.0001309362,0.9977167,0.00002131054,0.00001188454,0.001656045],"study_design_scores_gemma":[0.000006335989,0.0002348297,0.002502999,0.000005913789,0.00001532823,0.0001542983,0.00004835542,0.001259431,0.9948021,0.00005635095,0.000908047,0.000006159814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618893,0.000859146,0.03353671,0.0001486437,0.00001647855,0.0001555584,0.00149669,0.0002942693,0.001603114],"genre_scores_gemma":[0.9654393,0.001417293,0.02933824,0.00006618725,0.000008408796,0.00008619693,0.001878169,0.00004783638,0.001718501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007041066,"threshold_uncertainty_score":0.002355456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01888986852712062,"score_gpt":0.2609440288827996,"score_spread":0.2420541603556789,"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."}}