{"id":"W4382631698","doi":"10.1093/bioinformatics/btad266","title":"Reference panel-guided super-resolution inference of Hi-C data","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données","keywords":"Computer science; Inference; Focus (optics); Data mining; DNA sequencing; Chromatin; Task (project management); Computational biology; Resolution (logic); Genome; Artificial intelligence; DNA; Biology; Gene; Genetics; Physics","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":[0.003918025,0.001261817,0.001139702,0.002102887,0.0009062354,0.001654369,0.003928682,0.001892554,0.005181492],"category_scores_gemma":[0.01163721,0.0007846954,0.001167577,0.00159429,0.0007707826,0.001754123,0.002514632,0.002233671,0.002696381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167535,"about_ca_system_score_gemma":0.001808468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01524272,"about_ca_topic_score_gemma":0.02697579,"domain_scores_codex":[0.9988306,0.0002820655,0.0000466999,0.000467461,0.0002386701,0.0001345706],"domain_scores_gemma":[0.9959652,0.00173091,0.0002327675,0.0009102178,0.0009783062,0.0001825143],"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":[0.00158426,0.0005584027,0.05980469,0.001973461,0.001026015,0.0008730569,0.0005303343,0.3767913,0.08259871,0.01572703,0.1254262,0.3331065],"study_design_scores_gemma":[0.00006623067,0.000087805,0.009313574,0.0001089614,0.0001167141,0.0002679624,0.00008589952,0.936489,0.0250754,0.01412127,0.01420003,0.000067105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09062608,0.002897651,0.8489328,0.0009692041,0.0001833434,0.0001963717,0.02323771,0.02849899,0.004457829],"genre_scores_gemma":[0.3613693,0.001066697,0.5352361,0.001452912,0.0001561058,0.0004942943,0.09326652,0.002379177,0.004578913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01524272,"threshold_uncertainty_score":0.03030801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08873438610513366,"score_gpt":0.3032298105770997,"score_spread":0.214495424471966,"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."}}