{"id":"W4400455503","doi":"10.1038/s42003-024-06428-7","title":"SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data","year":2024,"lang":"en","type":"article","venue":"Communications Biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; U.S. Department of Health and Human Services; National Institutes of Health; National Institute on Drug Abuse; National Institute of Allergy and Infectious Diseases; Division of Intramural Research, National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute","keywords":"Chromatin; Chromosome conformation capture; Imputation (statistics); Computational biology; Pairwise comparison; Computer science; Cluster analysis; Multiplexing; Data mining; DNA; Enhancer; Biology; Missing data; Gene; Genetics; Artificial intelligence; Gene expression; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008386038,0.002197902,0.002073618,0.003608313,0.001839222,0.002887588,0.00522577,0.002264037,0.01265699],"category_scores_gemma":[0.03551967,0.00209875,0.003265367,0.00394115,0.001106318,0.002325642,0.004984001,0.004365767,0.01000483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009957962,"about_ca_system_score_gemma":0.003252269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004074977,"about_ca_topic_score_gemma":0.007345969,"domain_scores_codex":[0.9972026,0.0007612744,0.0002500725,0.0009297871,0.0006608005,0.0001953581],"domain_scores_gemma":[0.9907396,0.004114464,0.0007916393,0.002791446,0.001302792,0.0002600114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00280597,0.0004520664,0.07476381,0.002986908,0.004851792,0.00235954,0.001335151,0.1233865,0.04865967,0.02681143,0.2198609,0.4917263],"study_design_scores_gemma":[0.0004777162,0.0002798827,0.01308186,0.0003597751,0.0005712269,0.001686506,0.000278931,0.7723094,0.04746241,0.05505682,0.1079813,0.000454219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009643538,0.0006066205,0.9164083,0.0003353741,0.0001832453,0.0001565776,0.01609936,0.05562165,0.0009453939],"genre_scores_gemma":[0.07393242,0.000541514,0.8403945,0.0006196254,0.0001505745,0.001189221,0.06841263,0.01123287,0.003526586],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01265699,"threshold_uncertainty_score":0.04435009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093650897743724,"score_gpt":0.4079884387915541,"score_spread":0.2986233490171816,"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."}}