{"id":"W4390971113","doi":"10.1109/bibm58861.2023.10385885","title":"Data-driven trait heritability-based extraction of human facial phenotypes","year":2023,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"National Institute of Dental and Craniofacial Research; KU Leuven; Wellcome Trust","keywords":"Heritability; Trait; Principal component analysis; Quantitative trait locus; Missing heritability problem; Dimensionality reduction; Computer science; Artificial intelligence; Biology; Pattern recognition (psychology); Genetics; Genetic variants; Genotype; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.0009265088,0.0006435614,0.0005200367,0.0008667693,0.0001905601,0.0004115639,0.000431046,0.0004728801,0.0005984651],"category_scores_gemma":[0.00286624,0.0002393108,0.0006996772,0.0005287477,0.0003272097,0.0003214298,0.0005471212,0.0005074822,0.0002574771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003453612,"about_ca_system_score_gemma":0.0004890665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002595301,"about_ca_topic_score_gemma":0.002509779,"domain_scores_codex":[0.9995083,0.0001470718,0.00002368526,0.0001824256,0.00009298739,0.00004564122],"domain_scores_gemma":[0.998722,0.0007036907,0.0001485736,0.0001982687,0.0001958192,0.00003158849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004342136,0.0003544834,0.06186961,0.0002151138,0.0003151535,0.0004363611,0.0002833426,0.4220817,0.1506402,0.00369998,0.002246863,0.3574229],"study_design_scores_gemma":[0.00002055388,0.0000754596,0.03963738,0.00001265415,0.00004865496,0.000187879,0.00004267064,0.9340788,0.02329766,0.001945938,0.0006218387,0.00003051197],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3847965,0.0002133934,0.6121478,0.0001443868,0.0000166309,0.00006949427,0.0008639416,0.001074464,0.000673393],"genre_scores_gemma":[0.813405,0.0001138256,0.1829212,0.00005444125,0.00001504803,0.0001239292,0.002632897,0.0001085133,0.0006251076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002595301,"threshold_uncertainty_score":0.005160391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06031381968878462,"score_gpt":0.3468977623952834,"score_spread":0.2865839427064987,"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."}}