{"id":"W3049266977","doi":"10.1101/2020.08.14.251835","title":"WgLink: reconstructing whole-genome viral haplotypes using <i>L</i> <sub>0</sub> + <i>L</i> <sub>1</sub> -regularization","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genome; Haplotype; Source code; Computer science; Computational biology; Regularization (linguistics); Code (set theory); 1000 Genomes Project; Biology; Algorithm; Genetics; Allele; Artificial intelligence; Genotype; Gene; Single-nucleotide polymorphism; Set (abstract data type)","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":[0.001606058,0.001268621,0.0006856201,0.001077586,0.0004771292,0.001377067,0.001810907,0.001466744,0.003925677],"category_scores_gemma":[0.004426871,0.0006496948,0.001028751,0.001025261,0.0007368259,0.001106035,0.00179826,0.001401054,0.001858042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004639416,"about_ca_system_score_gemma":0.0009583132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004969893,"about_ca_topic_score_gemma":0.006660509,"domain_scores_codex":[0.9995269,0.0001292212,0.00001772739,0.000150061,0.0001431163,0.00003298539],"domain_scores_gemma":[0.9989674,0.0005068752,0.00009449124,0.00023967,0.000108833,0.00008282235],"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":[0.000529009,0.0002798605,0.01002149,0.0006358117,0.0005163835,0.0003331756,0.0002907582,0.6820936,0.04583579,0.01160678,0.03671275,0.2111446],"study_design_scores_gemma":[0.00003495611,0.00002336875,0.0006436995,0.00001104262,0.00001028653,0.00003197567,0.00002550748,0.9856456,0.007351308,0.003420551,0.002784634,0.00001707247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05932266,0.0002861994,0.9093313,0.0003079793,0.0001197041,0.00007549109,0.002776836,0.02581313,0.001966795],"genre_scores_gemma":[0.2627769,0.0001980286,0.7105958,0.0002967758,0.00008436866,0.0002254767,0.01344126,0.007144268,0.005237083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004969893,"threshold_uncertainty_score":0.01313269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317405316880677,"score_gpt":0.2018267044946703,"score_spread":0.1886526513258635,"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."}}