{"id":"W2547528004","doi":"10.1101/043430","title":"Computational Pan-Genomics: Status, Promises and Challenges","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Lorentz Center; Academy of Finland; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Koninklijke Nederlandse Akademie van Wetenschappen","keywords":"Genomics; Data science; Computer science; Computational genomics; Genome; Construct (python library); Homo sapiens; Computational biology; Computational model; Biology; Artificial intelligence; Genetics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000220083,0.0004352611,0.0003662895,0.00007821312,0.0001324722,0.00007665288,0.0002610222,0.0003752992,0.000005011986],"category_scores_gemma":[0.00007928653,0.0004103232,0.0000966676,0.00003772699,0.00018508,0.00000167407,0.0008059616,0.0001648885,0.00001063566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005515759,"about_ca_system_score_gemma":0.0004108583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009157396,"about_ca_topic_score_gemma":0.000002937073,"domain_scores_codex":[0.9980735,0.00007305823,0.0003194943,0.0009389197,0.0001496262,0.0004454546],"domain_scores_gemma":[0.9986503,0.00002932131,0.0002274311,0.0006304579,0.0002774429,0.0001850862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004766573,0.00006637401,0.006353012,0.0002869759,0.0004095117,0.000005438279,0.00001617464,0.00005970132,0.9917675,0.0007098115,0.0002328128,0.00004499125],"study_design_scores_gemma":[0.00177953,0.0003456118,0.469478,0.0002829944,0.0001963809,9.641532e-8,0.0000179623,0.00009496477,0.3389003,0.0001753957,0.1867133,0.002015557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506441,0.04692802,0.0003724074,0.0004731982,0.000460941,0.0004757768,0.0005863461,0.00002585189,0.00003333544],"genre_scores_gemma":[0.9640321,0.03118305,0.003838273,0.0001111997,0.0006165769,0.0001243292,0.000001096049,0.00008675299,0.000006628061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6528673,"threshold_uncertainty_score":0.9998348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801472310791249,"score_gpt":0.2168702063329543,"score_spread":0.1988554832250418,"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."}}