{"id":"W4409999892","doi":"10.1101/2025.04.28.651068","title":"iSHARC: Integrating scMultiome data for heterogeneity and regulatory analysis in cancer","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Institutes of Health Research; Princess Margaret Cancer Foundation","keywords":"Data science; Computer science","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":[0.004506345,0.001209437,0.001181612,0.003632868,0.0008211773,0.00278515,0.001555243,0.0008867375,0.01229914],"category_scores_gemma":[0.005500706,0.00100523,0.001806,0.002372698,0.0005704133,0.001393964,0.003228213,0.001796896,0.008937182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009726342,"about_ca_system_score_gemma":0.0023351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002970571,"about_ca_topic_score_gemma":0.003368019,"domain_scores_codex":[0.9978708,0.0002965921,0.0001549585,0.0007103247,0.0007918095,0.0001755275],"domain_scores_gemma":[0.9965008,0.00110222,0.0002570623,0.00106224,0.0007843281,0.000293207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002427727,0.0002129473,0.01646264,0.002703383,0.0008327637,0.0007913424,0.0009663989,0.02422752,0.2792687,0.01508883,0.3855287,0.271489],"study_design_scores_gemma":[0.0004077461,0.0002327392,0.03198102,0.0003498343,0.0004056145,0.0007748704,0.0003842281,0.2005185,0.3702686,0.03975508,0.354398,0.0005237191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03364121,0.000732855,0.5918352,0.00113593,0.0003078362,0.0003591238,0.1050803,0.2613217,0.005585882],"genre_scores_gemma":[0.1061156,0.0005086816,0.6338944,0.0005363611,0.0001757474,0.0009583553,0.2164447,0.03607081,0.005295343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01229914,"threshold_uncertainty_score":0.04114473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03180626602976076,"score_gpt":0.3035294462192298,"score_spread":0.271723180189469,"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."}}