{"id":"W4224006630","doi":"10.1101/2022.04.14.488414","title":"Integrative chromatin domain annotation through graph embedding of Hi-C data","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Annotation; Chromatin; Genome; Computational biology; Embedding; Domain (mathematical analysis); Pairwise comparison; Genomics; Graph; Biology; Computer science; Epigenomics; Genetics; Artificial intelligence; Theoretical computer science; Gene; DNA methylation; Gene expression; Mathematics","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":[0.0004718031,0.0009339926,0.0005070816,0.002192866,0.0003972379,0.0008463395,0.001378353,0.001062173,0.003121011],"category_scores_gemma":[0.002155686,0.0003518568,0.000801186,0.001599766,0.00042876,0.0009708663,0.001004947,0.001046034,0.001422324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007235966,"about_ca_system_score_gemma":0.0005357073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006547253,"about_ca_topic_score_gemma":0.01178617,"domain_scores_codex":[0.9996319,0.00007053526,0.00001127194,0.0001819712,0.00006652115,0.00003773043],"domain_scores_gemma":[0.9989814,0.0004203622,0.00008240153,0.0002556438,0.0001906806,0.00006952908],"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":[0.000923414,0.0004352783,0.02776434,0.001373616,0.0003872533,0.0007679776,0.0004645141,0.4915413,0.1082628,0.0252826,0.05542549,0.2873713],"study_design_scores_gemma":[0.0000162645,0.00003744974,0.003605146,0.00002868941,0.0000338489,0.00009706501,0.00005886662,0.9606987,0.01227181,0.01544452,0.007678108,0.0000294979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1395538,0.0007035371,0.8045317,0.0006148943,0.00009735946,0.000193054,0.03319198,0.01745061,0.003663108],"genre_scores_gemma":[0.5263872,0.0003478023,0.3921618,0.0002678241,0.00005245304,0.0002243684,0.07607073,0.001406003,0.00308178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006547253,"threshold_uncertainty_score":0.01301825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475850079598979,"score_gpt":0.2537493277210811,"score_spread":0.2389908269250913,"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."}}