{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008937628,0.0001172652,0.0003400238,0.000402244,0.00002987733,0.00001321412,0.0004106307,0.000159895,0.000002895501],"category_scores_gemma":[0.0001965416,0.0001036499,0.0000704449,0.000346954,0.00006639076,0.00001445061,0.0004427459,0.00005031881,2.246713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001986027,"about_ca_system_score_gemma":0.0001175687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000707052,"about_ca_topic_score_gemma":0.000001729073,"domain_scores_codex":[0.9985592,0.0001749972,0.0006483575,0.0002955573,0.0001960085,0.0001258205],"domain_scores_gemma":[0.9980206,0.0000112399,0.0007874353,0.000746235,0.0002921516,0.000142375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002775282,0.00004627772,0.001368703,0.00002749448,0.0001396855,0.000002554537,0.00005641841,0.0005791279,0.9881126,0.00000668015,0.00007382019,0.009309115],"study_design_scores_gemma":[0.0005030893,0.0001711184,0.002035627,0.00003010481,0.0002632254,0.00003106082,0.0001521479,0.0002994385,0.9945251,0.00006310963,0.001832438,0.00009350735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5699013,0.001289328,0.4284781,0.0001152742,0.00009296476,0.00008945474,0.00002935184,0.000003084789,0.000001134771],"genre_scores_gemma":[0.90013,0.0002282294,0.09946696,0.00002033203,0.00005984494,0.000006572753,0.00006780677,0.00001058794,0.00000962464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3302287,"threshold_uncertainty_score":0.4226718,"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."}}