{"id":"W4394360790","doi":"10.6084/m9.figshare.23751836","title":"Additional file 1 of Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Regulon; Atlas (anatomy); Omics; Adenocarcinoma; Computational biology; Stratification (seeds); Risk stratification; Computer science; Internal medicine; Oncology; Data mining; Biology; Bioinformatics; Medicine; Cancer; Genetics; Dormancy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001566901,0.001540943,0.001555442,0.002320495,0.0008654378,0.001768266,0.002313867,0.001629313,0.5122333],"category_scores_gemma":[0.0117724,0.0005573659,0.001290916,0.003615601,0.0003682089,0.001333594,0.001338459,0.001412062,0.09521966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333964,"about_ca_system_score_gemma":0.002495194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007324474,"about_ca_topic_score_gemma":0.01875106,"domain_scores_codex":[0.9992293,0.0001205235,0.0001117637,0.0002728126,0.000152307,0.0001132366],"domain_scores_gemma":[0.9937476,0.004224812,0.0003852207,0.0005903763,0.0007638369,0.0002880845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003020731,0.00007117082,0.002855144,0.002932508,0.0001082084,0.00008388531,0.00004482288,0.0006985681,0.0003552651,0.0006918463,0.9864144,0.005442133],"study_design_scores_gemma":[0.003930142,0.0002798085,0.02695291,0.002212306,0.0004444688,0.0007184533,0.0002467698,0.003052403,0.002470043,0.01075856,0.9487687,0.0001653683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000923519,0.00002124363,0.00009737603,0.00003206904,0.000008645285,0.00001814346,0.999437,0.0001267625,0.000166327],"genre_scores_gemma":[0.001567178,0.00006350796,0.0009248363,0.0001475145,0.00002062708,0.0004053817,0.9957258,0.0001575137,0.0009876895],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5122333,"threshold_uncertainty_score":0.69574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08061342085937294,"score_gpt":0.3506914692088502,"score_spread":0.2700780483494773,"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."}}