{"id":"W4389703453","doi":"10.1101/2023.12.12.571388","title":"scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fonds de Recherche du Québec - Santé; Shandong University","keywords":"Chromatin; Computer science; Computational biology; Cell cycle; Context (archaeology); ChIA-PET; Cell; Artificial intelligence; Biology; Gene; Chromatin remodeling; Genetics","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":[0.0006712401,0.0009811686,0.0007049714,0.0008042829,0.0003947048,0.0008616643,0.001662864,0.001108809,0.003237289],"category_scores_gemma":[0.001387928,0.0005527447,0.0007381935,0.0006525565,0.0004125705,0.0007469284,0.0008467056,0.001237711,0.001177797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009501038,"about_ca_system_score_gemma":0.001631292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181742,"about_ca_topic_score_gemma":0.02356245,"domain_scores_codex":[0.9998678,0.00002618507,0.000005184003,0.00004892593,0.00002872819,0.00002324851],"domain_scores_gemma":[0.9996791,0.0001526468,0.00002363367,0.00004219123,0.00006367704,0.00003889716],"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":[0.0006103848,0.0002077282,0.009777998,0.0002878565,0.0002986163,0.0001993709,0.00006242147,0.6822258,0.01397476,0.008690352,0.03023623,0.2534284],"study_design_scores_gemma":[0.000009332462,0.00001955141,0.0002947726,0.00000586919,0.00000623792,0.00001329352,0.000003749914,0.9944988,0.001184727,0.002977816,0.0009789636,0.000006908253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06430651,0.001523266,0.9018042,0.0006610174,0.0001213233,0.0001205613,0.008097379,0.02073876,0.002627009],"genre_scores_gemma":[0.5409372,0.0009079045,0.4226039,0.0007465456,0.0001216207,0.0004413468,0.02505399,0.001264525,0.007923005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01181742,"threshold_uncertainty_score":0.02349728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04565435250200858,"score_gpt":0.2657495126113392,"score_spread":0.2200951601093306,"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."}}