{"id":"W2810056093","doi":"10.5772/intechopen.75311","title":"Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data","year":2018,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Support vector machine; Artificial intelligence; dNaM; Computer science; Artificial neural network; Deep learning; Omics; Random forest; Predictive modelling; Regression; DNA methylation; Computational biology; Bioinformatics; Data mining; Biology; Gene; Mathematics; Gene expression; Statistics","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":[0.00006696364,0.0002178838,0.0001812149,0.00004047741,0.0001050436,0.00004372415,0.0003458899,0.0003392358,0.00002584342],"category_scores_gemma":[0.00005473352,0.0002157759,0.00006037111,0.000003432827,0.0001068528,0.000004035719,0.0003257738,0.0001519546,0.000002831554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001302094,"about_ca_system_score_gemma":0.00007093901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000460409,"about_ca_topic_score_gemma":0.00002428077,"domain_scores_codex":[0.9988765,0.0000125708,0.0002145038,0.0006549617,0.00009812321,0.0001433091],"domain_scores_gemma":[0.9991866,0.00002294954,0.0001790001,0.000431519,0.000095166,0.00008473123],"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":[0.0004750998,0.00001890896,0.0000292701,0.0001131887,0.000350963,0.000001371115,0.0003416879,0.00002434061,0.7490066,0.001341596,0.003351382,0.2449456],"study_design_scores_gemma":[0.00119188,0.0004251209,0.00004279925,0.0004145904,0.000393249,0.000006062544,0.0002320082,0.0143092,0.1133106,0.06045851,0.8083014,0.0009145373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01164032,0.01380572,0.8003564,0.0001035377,0.0007766956,0.001826061,0.003177564,0.000211575,0.1681021],"genre_scores_gemma":[0.7708686,0.0007333659,0.004676713,0.0003453548,0.002764046,0.0001366919,0.00650592,0.0002293037,0.21374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8049501,"threshold_uncertainty_score":0.8799086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04591464419058579,"score_gpt":0.2681424566172354,"score_spread":0.2222278124266496,"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."}}