{"id":"W4412939890","doi":"10.1109/jbhi.2025.3595101","title":"KAFSTExp: Kernel Adaptive Filtering With Nyström Approximation for Predicting Spatial Gene Expression From Histology Images","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Michael Smith Health Research BC","keywords":"Kernel (algebra); Artificial intelligence; Computer science; Pattern recognition (psychology); Image (mathematics); Computer vision; Mathematics","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":[0.0003512633,0.0001005965,0.0002090389,0.0001052932,0.0001212877,0.00001892581,0.0001063864,0.0001235894,0.000003237092],"category_scores_gemma":[0.00004264196,0.00007174626,0.00003971574,0.00005919654,0.00008339756,0.00001367707,0.00003179341,0.0001206977,2.135757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003851808,"about_ca_system_score_gemma":0.0003027944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001711356,"about_ca_topic_score_gemma":0.000004606897,"domain_scores_codex":[0.9988694,0.00003140791,0.0006901213,0.00008958447,0.0001637516,0.0001557557],"domain_scores_gemma":[0.9989241,0.00002568518,0.0006656122,0.0001054366,0.0001427225,0.0001364166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001733761,0.0001526293,0.001648579,0.0007504567,0.0001571076,0.000001588273,0.001832838,0.0001542829,0.6982043,0.0000479782,0.02707218,0.2682443],"study_design_scores_gemma":[0.009568755,0.00561775,0.009180685,0.002010277,0.0001186328,0.0001178376,0.003912435,0.0202668,0.8251143,0.0007761748,0.1228298,0.000486608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1637937,0.0005997135,0.8333256,0.001565027,0.0004253197,0.0001864628,0.00004088421,0.000004444755,0.00005884313],"genre_scores_gemma":[0.9225813,0.0005543268,0.07522689,0.0008750734,0.0005942498,0.00002115507,0.00007758687,0.000008565635,0.00006084574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7587876,"threshold_uncertainty_score":0.2925727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124604237702496,"score_gpt":0.2955878767487929,"score_spread":0.274341834371768,"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."}}