{"id":"W4307373120","doi":"10.3390/cancers14215235","title":"Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape","year":2022,"lang":"en","type":"article","venue":"Cancers","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Alberta","funders":"","keywords":"Tumor heterogeneity; Computational biology; Epigenetics; Genetic heterogeneity; Phenotype; Transcriptome; Spatial heterogeneity; Biology; Visualization; Computer science; Gene; Cancer; Bioinformatics; Data mining; Gene expression; Genetics; Ecology","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":[0.00007113847,0.00006487919,0.0001316067,0.00004948222,0.00005601683,0.000005143449,0.00007350407,0.00001993157,0.00003475498],"category_scores_gemma":[0.000009315884,0.00007023097,0.00007247949,0.0001352858,0.00003423514,8.117343e-7,0.00005207761,0.00004882433,8.990846e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000311945,"about_ca_system_score_gemma":0.00008118912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001779455,"about_ca_topic_score_gemma":0.00006640122,"domain_scores_codex":[0.9994913,0.00003088022,0.0001219536,0.000163071,0.00008738271,0.0001053684],"domain_scores_gemma":[0.999765,0.000004075304,0.0000322498,0.0001293781,0.00002400782,0.0000453182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001437038,0.00001625293,0.01400253,0.00001697425,0.0005288279,0.000007380509,0.00007625961,0.4851076,0.499303,0.0001032607,0.00006975237,0.0006244134],"study_design_scores_gemma":[0.0008172972,0.000387972,0.001367807,0.000003653597,0.0007621208,0.000009775582,0.0003659763,0.9045159,0.08979543,0.00005538754,0.001604615,0.0003141057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787419,0.001005056,0.0198853,0.0000100678,0.000053097,0.00004261943,0.000139772,0.000003553197,0.0001186725],"genre_scores_gemma":[0.9991326,0.00004244229,0.0004658987,0.0001912732,0.00001477815,0.0000113945,0.0001229303,0.000008233055,0.00001044363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4194083,"threshold_uncertainty_score":0.2863936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287730311445116,"score_gpt":0.2443846608595486,"score_spread":0.2315073577450975,"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."}}