{"id":"W4319949177","doi":"10.1101/2023.02.08.527583","title":"Adaptive Digital Tissue Deconvolution","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; European Commission; Trond Mohn stiftelse; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Deconvolution; Computer science; Python (programming language); Inference; Data type; Artificial intelligence; Computational biology; Machine learning; Biology; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007340204,0.0007001704,0.0004444536,0.000659137,0.0002467042,0.001073202,0.001133869,0.0008062531,0.004735176],"category_scores_gemma":[0.001850008,0.0003234195,0.0007539446,0.0006502508,0.0008373093,0.0008530921,0.001276109,0.001058442,0.00203796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006857187,"about_ca_system_score_gemma":0.0007939803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266578,"about_ca_topic_score_gemma":0.001508819,"domain_scores_codex":[0.999656,0.00006116965,0.00002038175,0.00009689976,0.0001388866,0.00002674685],"domain_scores_gemma":[0.9994436,0.0002131371,0.0000511899,0.0001250059,0.000131178,0.0000358733],"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":[0.0003790751,0.00007088812,0.003230661,0.0007206446,0.0001163211,0.0003870829,0.0001982078,0.3742235,0.1434243,0.04553856,0.01079176,0.420919],"study_design_scores_gemma":[0.00001600625,0.0000237394,0.0004734988,0.00002803094,0.00001611778,0.0003046875,0.00001678449,0.9315622,0.04148214,0.01640905,0.009635794,0.00003196436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003224656,0.0001506831,0.9934563,0.0001253316,0.00003214936,0.00002284023,0.0002357538,0.001233596,0.001518672],"genre_scores_gemma":[0.1229291,0.0005967125,0.868874,0.000312112,0.00008348915,0.00011809,0.001199808,0.000515726,0.005370856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004735176,"threshold_uncertainty_score":0.01584071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045183574098224,"score_gpt":0.2214160749978566,"score_spread":0.2009642392568743,"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."}}