{"id":"W2887335093","doi":"10.1101/392407","title":"SonHi-C: a set of non-procedural approaches for predicting 3D genome organization from Hi-C data","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Leverage (statistics); Set (abstract data type); Implementation; Schizosaccharomyces pombe; Integer programming; Constraint programming; Constraint (computer-aided design); Matching (statistics); Theoretical computer science; Genome; Biological data; Programming language; Artificial intelligence; Mathematical optimization; Algorithm; Yeast; Mathematics; Biology; Bioinformatics; Saccharomyces cerevisiae","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":[0.002009145,0.001587889,0.0008434774,0.00169275,0.0006668192,0.00165499,0.004187872,0.001222483,0.004944855],"category_scores_gemma":[0.00466749,0.0008904996,0.002181984,0.001555021,0.001001121,0.00144775,0.001838297,0.001846216,0.0009707647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001412046,"about_ca_system_score_gemma":0.003235339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01302028,"about_ca_topic_score_gemma":0.01689438,"domain_scores_codex":[0.99888,0.0002691164,0.00007854756,0.0002355496,0.0004616858,0.00007510258],"domain_scores_gemma":[0.9973624,0.001317398,0.000267713,0.0004133325,0.0005119961,0.0001271552],"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":[0.0001561935,0.0002861652,0.00309225,0.0004004324,0.0002159779,0.0001787495,0.0001380591,0.8136637,0.01172669,0.02689706,0.009145209,0.1340995],"study_design_scores_gemma":[0.000009258848,0.00002256989,0.0001540028,0.000008805859,0.000006878414,0.00001908405,0.00001682147,0.9923075,0.003396006,0.002539962,0.001506774,0.00001229036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01303807,0.0001195176,0.9773716,0.0001590072,0.00002529586,0.0001772859,0.0005531129,0.006229201,0.002327035],"genre_scores_gemma":[0.0521174,0.000139447,0.9441693,0.000106974,0.00001222491,0.0003027756,0.001508329,0.0006218298,0.001021759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01302028,"threshold_uncertainty_score":0.02588898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290577523062152,"score_gpt":0.2215280326500288,"score_spread":0.1986222574194073,"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."}}