{"id":"W4206809292","doi":"10.3390/cancers14030464","title":"Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics","year":2022,"lang":"en","type":"article","venue":"Cancers","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"","keywords":"Connectomics; Neuroimaging; Normative; Magnetic resonance imaging; Neuroscience; Connectome; Medicine; Computer science; Psychology; Medical physics; Radiology; Functional connectivity","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":[0.0001246225,0.0001627207,0.0001891228,0.00003832335,0.0002815515,0.00004544606,0.0001913047,0.00006865519,0.00002078944],"category_scores_gemma":[0.00006402817,0.0001920095,0.0001001325,0.00006193254,0.0001043418,0.000006926143,0.00006562578,0.0001649878,4.63858e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969368,"about_ca_system_score_gemma":0.0004387572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000245263,"about_ca_topic_score_gemma":0.0002297401,"domain_scores_codex":[0.9989446,0.00006234758,0.0001930811,0.0004229495,0.00008592729,0.0002910833],"domain_scores_gemma":[0.9995509,0.00006056289,0.00006782154,0.000192199,0.00003789595,0.00009055328],"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":[0.0007486144,0.00002086276,0.000581161,0.00002694212,0.00004031427,0.000005202083,0.0002005552,0.002189008,0.962404,0.0001057491,0.003084359,0.03059321],"study_design_scores_gemma":[0.003564743,0.001226533,0.0002707904,0.000004099448,0.00007362884,0.00005507889,0.0004076262,0.01324429,0.09153689,0.00012291,0.8890681,0.0004252694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830531,0.0005987193,0.01309029,0.000700901,0.001281008,0.0005209607,0.0003604688,0.00002821635,0.0003662798],"genre_scores_gemma":[0.9939819,0.0002419186,0.0005217707,0.004485084,0.0002762217,0.0001484855,0.0002041447,0.00004759075,0.00009290421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8859838,"threshold_uncertainty_score":0.7829919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637745687204918,"score_gpt":0.2598675742986232,"score_spread":0.233490117426574,"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."}}