{"id":"W4392037827","doi":"10.3389/fbinf.2023.1285828","title":"Posterior inference of Hi-C contact frequency through sampling","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Downstream (manufacturing); Sampling (signal processing); Posterior probability; Computer science; Biological system; Algorithm; Mathematics; Artificial intelligence; Biology; Engineering; Bayesian probability; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0001261226,0.0001398532,0.0001870038,0.00007545885,0.00002466538,0.00004386901,0.0002145388,0.0001438285,0.000007706478],"category_scores_gemma":[0.00004948998,0.0001323851,0.0000765646,0.0001297348,0.00005461767,0.00001684083,0.00009065037,0.0001047954,0.000005543909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003511887,"about_ca_system_score_gemma":0.000121344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001786506,"about_ca_topic_score_gemma":0.00001043624,"domain_scores_codex":[0.999105,0.00001137861,0.0004497515,0.0001325656,0.00009761266,0.0002036713],"domain_scores_gemma":[0.9995618,0.00001159912,0.00008983908,0.0002638568,0.00003976536,0.00003311317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003037163,0.0004559223,0.1792266,0.008332154,0.001309685,0.00005228137,0.02104027,0.004852043,0.5564556,0.02175937,0.02541697,0.1807954],"study_design_scores_gemma":[0.005616908,0.003814425,0.03655562,0.003595139,0.0003533062,0.0002237792,0.01623955,0.6585548,0.1223111,0.04480743,0.1031668,0.004761097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8291638,0.002317224,0.1639163,0.00005031727,0.001052913,0.0001978947,0.0001119228,0.00001931054,0.003170267],"genre_scores_gemma":[0.7894003,0.0005906862,0.2096797,0.00009237289,0.00004355017,0.000006697215,0.0001142484,0.00001614073,0.00005625339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6537027,"threshold_uncertainty_score":0.539851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151197932171218,"score_gpt":0.2579589917671961,"score_spread":0.2464470124454839,"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."}}