{"id":"W2284585940","doi":"10.3762/bjnano.6.252","title":"Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials","year":2015,"lang":"en","type":"article","venue":"Beilstein Journal of Nanotechnology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Toxicogenomics; Transcriptome; Gene expression; DNA damage; Gene; DNA microarray; Microarray analysis techniques; Gene expression profiling; Pulmonary fibrosis; Fibrosis; Computational biology; Microarray; Biology; Bioinformatics; Medicine; Genetics; DNA; Pathology","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":[0.003965222,0.001302963,0.001857146,0.006370219,0.000894101,0.001605791,0.001005629,0.0006067433,0.001669125],"category_scores_gemma":[0.009046869,0.000408984,0.002687206,0.004146295,0.0008777087,0.0005428867,0.001522365,0.001561349,0.0008933809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006446445,"about_ca_system_score_gemma":0.001608215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002345413,"about_ca_topic_score_gemma":0.003039431,"domain_scores_codex":[0.9970078,0.0007930064,0.000306931,0.001017538,0.0007028559,0.0001719449],"domain_scores_gemma":[0.9933141,0.004281545,0.0007421908,0.000688744,0.000739926,0.0002334035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003185465,0.001097439,0.1683971,0.00497625,0.007430776,0.001568938,0.001775891,0.06402735,0.3174464,0.006371863,0.01200511,0.4117175],"study_design_scores_gemma":[0.0002417571,0.001390211,0.1745023,0.0003568838,0.001906677,0.001990583,0.001524017,0.6682947,0.09513447,0.03011429,0.02418093,0.0003631347],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2902476,0.003569795,0.6739812,0.0008914025,0.0002652488,0.0008798679,0.01953502,0.008213664,0.002416181],"genre_scores_gemma":[0.4594839,0.0009516159,0.5097983,0.0007069857,0.0001001248,0.001976685,0.02511671,0.0007359134,0.001129657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006370219,"threshold_uncertainty_score":0.0209704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04476864523512591,"score_gpt":0.3773156944955126,"score_spread":0.3325470492603867,"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."}}