{"id":"W4412024762","doi":"10.1038/s41467-025-61580-w","title":"DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal; McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Exon; Computational biology; Transcriptome; Gene; Biology; Computer science; Genetics; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001512514,0.0001814493,0.0001534948,0.00007262599,0.0003272326,0.00005426407,0.0005108435,0.0003853368,0.000003051305],"category_scores_gemma":[0.00004197846,0.0001910328,0.00007236972,0.0001776539,0.0001566724,0.00001613271,0.0001175715,0.0004819177,0.000001112212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003163505,"about_ca_system_score_gemma":0.00005535706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000267782,"about_ca_topic_score_gemma":0.0003701409,"domain_scores_codex":[0.9990863,0.0000862183,0.0002340707,0.0003202102,0.00008882442,0.0001844083],"domain_scores_gemma":[0.9988242,0.00004413744,0.00006793306,0.0008972862,0.0001116705,0.00005480416],"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":[0.00004837344,0.0001985749,0.001184467,0.00002593507,0.00003279701,1.656248e-7,0.00009302911,0.00001069939,0.9829982,0.0004426312,0.006158542,0.008806613],"study_design_scores_gemma":[0.0007915439,0.00007663357,0.0005447524,0.00002437064,0.00006199755,0.000005106478,0.0001153564,0.0002075286,0.4015075,0.0003118903,0.5961089,0.0002444576],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6340076,0.2736228,0.0468798,0.009150233,0.001755762,0.0008276258,0.000509089,0.0001419813,0.03310516],"genre_scores_gemma":[0.9784943,0.008809347,0.008393808,0.001323122,0.00005520564,0.00002350979,0.001036862,0.00002232265,0.001841558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5899503,"threshold_uncertainty_score":0.7790092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929745895645753,"score_gpt":0.2603318972671924,"score_spread":0.2410344383107348,"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."}}