{"id":"W2997918501","doi":"10.1073/pnas.1901493116","title":"DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Computer science; Natural (archaeology); Computational biology; Data science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00134705,0.001915328,0.001292142,0.00219758,0.000495664,0.001797752,0.001756577,0.001067379,0.002499865],"category_scores_gemma":[0.002232579,0.0009900758,0.001491049,0.001273511,0.0006703413,0.001981431,0.00292443,0.00209455,0.001566934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000937579,"about_ca_system_score_gemma":0.00171211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002286674,"about_ca_topic_score_gemma":0.00410743,"domain_scores_codex":[0.9991605,0.0000963366,0.00004066036,0.00038835,0.0002289429,0.00008520652],"domain_scores_gemma":[0.9991059,0.0003351438,0.0001430334,0.0001572686,0.0001596082,0.00009904375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002491874,0.0009523728,0.03025717,0.001801682,0.00126677,0.001332791,0.0003159781,0.1206547,0.474327,0.007059576,0.02557604,0.3339641],"study_design_scores_gemma":[0.0001811991,0.0005342855,0.01017832,0.00006247184,0.0002175055,0.0003673921,0.0001100152,0.7771441,0.1732744,0.01922034,0.01856522,0.0001447806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2569439,0.002580788,0.6180509,0.001391423,0.0001705128,0.0004085442,0.03745558,0.07636679,0.006631513],"genre_scores_gemma":[0.3758495,0.001443208,0.5619704,0.001283541,0.00009154244,0.000501063,0.05455766,0.002028702,0.002274343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002499865,"threshold_uncertainty_score":0.008362949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05890757804733093,"score_gpt":0.3148766347755096,"score_spread":0.2559690567281787,"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."}}