{"id":"W4283659942","doi":"10.1093/bioinformatics/btac420","title":"Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Candidate gene; Aspergillus nidulans; Computer science; Task (project management); Reinforcement learning; Genome; Computational biology; Gene; Artificial intelligence; Biology; Genetics; Engineering","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":[0.00152741,0.001004375,0.0007632046,0.0005643406,0.0004488892,0.0007222955,0.001112069,0.000759878,0.001684129],"category_scores_gemma":[0.004855495,0.0002947809,0.0006123211,0.0004465268,0.0005629805,0.0007294838,0.0009210005,0.001037553,0.0007396427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100279,"about_ca_system_score_gemma":0.001502522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438917,"about_ca_topic_score_gemma":0.00668074,"domain_scores_codex":[0.9994641,0.0001277698,0.00002310435,0.0002333929,0.00009925593,0.0000524119],"domain_scores_gemma":[0.9981812,0.001036067,0.000167056,0.0001926018,0.0003043701,0.0001187059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007600203,0.0003008062,0.01709377,0.0004110979,0.0001209576,0.0001334004,0.0001025595,0.7443557,0.02691329,0.001833549,0.003909271,0.2040655],"study_design_scores_gemma":[0.00002705375,0.00005294478,0.0007675628,0.00001524794,0.00001772647,0.00002141383,0.000009659945,0.9898637,0.007141343,0.001508848,0.0005661459,0.000008308807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4137201,0.00196081,0.5675875,0.0008442676,0.0001209362,0.0001636167,0.001535528,0.0105644,0.003502851],"genre_scores_gemma":[0.771991,0.0002853756,0.2236714,0.0002919972,0.00003208587,0.00009128855,0.002104783,0.000275342,0.001256713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006438917,"threshold_uncertainty_score":0.01280284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007802561118249228,"score_gpt":0.2136643489196424,"score_spread":0.2058617878013932,"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."}}