{"id":"W3205549457","doi":"10.1101/2021.10.18.464903","title":"Inverse Potts model improves accuracy of phylogenetic profiling","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Research Organization of Information and Systems","keywords":"Spurious relationship; Phylogenetic tree; Correlation; Profiling (computer programming); Computer science; Data mining; Measure (data warehouse); Statistics; Artificial intelligence; Mathematics; Algorithm; Machine learning; Biology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005709888,0.001348033,0.001826095,0.002573462,0.0007778221,0.0023099,0.001732846,0.001720592,0.001970245],"category_scores_gemma":[0.01994983,0.0003479732,0.001451164,0.001689872,0.0007872926,0.004352648,0.00194753,0.002161583,0.0006156746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001664326,"about_ca_system_score_gemma":0.00108804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004527155,"about_ca_topic_score_gemma":0.002298631,"domain_scores_codex":[0.997229,0.0009629692,0.0001644378,0.000827048,0.0005503758,0.0002660845],"domain_scores_gemma":[0.9902837,0.006636764,0.0007196984,0.0009748176,0.001051574,0.0003334029],"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":[0.0006957229,0.0002855098,0.03087111,0.0003161393,0.000231181,0.0002326394,0.000155536,0.830998,0.007831924,0.01183897,0.00647694,0.1100664],"study_design_scores_gemma":[0.000005992637,0.00002518605,0.0008374906,0.000007220164,0.00001087161,0.00002111441,0.000009162096,0.9940817,0.00106822,0.003721739,0.0002024668,0.00000872238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4932338,0.002921375,0.4872428,0.001920889,0.0003627208,0.0001269512,0.001826845,0.004842411,0.007522243],"genre_scores_gemma":[0.9564509,0.0002349688,0.04024392,0.0001947525,0.00008928476,0.00004644745,0.001465114,0.0002302771,0.001044274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005709888,"threshold_uncertainty_score":0.03019714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539360008954159,"score_gpt":0.225453091257406,"score_spread":0.2100594911678644,"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."}}