{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02377463,0.001047357,0.001482091,0.002007612,0.001077843,0.007553279,0.004894237,0.003911457,0.005450575],"category_scores_gemma":[0.02794372,0.0007808778,0.001189424,0.003491803,0.006537193,0.01567219,0.006785924,0.007169968,0.00198323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001564049,"about_ca_system_score_gemma":0.003011068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549716,"about_ca_topic_score_gemma":0.0009688575,"domain_scores_codex":[0.9939519,0.003657915,0.0002085244,0.0008371206,0.001102438,0.0002421251],"domain_scores_gemma":[0.9590455,0.03228375,0.0005272416,0.003350693,0.003468358,0.00132439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001749294,0.0001624853,0.002782996,0.002186473,0.0002350831,0.0001110547,0.0005131924,0.02544836,0.001517579,0.5555037,0.04819294,0.3631712],"study_design_scores_gemma":[0.00005082888,0.00006926197,0.0009900006,0.001001246,0.00004009833,0.0002143009,0.000637915,0.06181524,0.001112684,0.7953582,0.1386375,0.0000727861],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0229242,0.4174147,0.307863,0.2258266,0.00232903,0.00009231384,0.0009025644,0.002468728,0.02017892],"genre_scores_gemma":[0.2573704,0.3284694,0.3852399,0.01421664,0.006186725,0.0003912145,0.002657189,0.001048396,0.004420243],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02377463,"threshold_uncertainty_score":0.1257337,"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."}}