{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004685137,0.0001306352,0.0001349933,0.00005831468,0.0001782315,0.00005687204,0.001458849,0.0002225253,0.000008222836],"category_scores_gemma":[0.0001521075,0.0001268593,0.00006706855,0.0001086738,0.0001309744,0.00001365098,0.0004360517,0.0001188554,0.000005773358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001276744,"about_ca_system_score_gemma":0.00008712106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006032067,"about_ca_topic_score_gemma":0.0005543244,"domain_scores_codex":[0.9988495,0.0002378622,0.0002429727,0.0004495825,0.00002951008,0.0001906172],"domain_scores_gemma":[0.9973022,0.0001699101,0.00004417949,0.002329472,0.000095177,0.00005901347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000476751,0.0001314538,0.0002607997,0.00003161673,0.0001061827,2.442832e-7,0.0001207883,0.0000244985,0.8627074,0.004244874,0.006247959,0.1260765],"study_design_scores_gemma":[0.0005735554,0.0004117537,0.0003249845,0.00001605462,0.00007500296,0.00001402902,0.00007914114,0.1618698,0.02864818,0.002331706,0.8053444,0.0003114206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05287217,0.004683526,0.9317636,0.005611664,0.0009978163,0.0008955827,0.002114563,0.0002028569,0.0008581857],"genre_scores_gemma":[0.7041655,0.0007503708,0.2644056,0.0007984698,0.0003027401,0.0001065569,0.02926834,0.00003840595,0.0001640827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8340592,"threshold_uncertainty_score":0.5173171,"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."}}