{"id":"W4313493331","doi":"10.1155/2023/5333361","title":"Identification of Smoking‐Associated Transcriptome Aberration in Blood with Machine Learning Methods","year":2023,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Chinese Academy of Sciences","keywords":"Feature selection; Gene isoform; Computational biology; Alternative splicing; Computer science; Bioinformatics; Biology; Artificial intelligence; Gene; Genetics","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":[0.0009546685,0.0004329601,0.0005804454,0.001644502,0.0002695768,0.0007558917,0.0003599795,0.0003207741,0.001019661],"category_scores_gemma":[0.001922703,0.0001568866,0.0008289953,0.001618,0.0001689212,0.0003396716,0.0003805384,0.0004416333,0.0005404245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002443646,"about_ca_system_score_gemma":0.0004194285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006828578,"about_ca_topic_score_gemma":0.0009032316,"domain_scores_codex":[0.9994925,0.0001155057,0.0000519179,0.0001828884,0.00009685001,0.00006022496],"domain_scores_gemma":[0.9994016,0.0002739633,0.0001239275,0.00006941512,0.0001056534,0.00002544574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008686167,0.0003857093,0.2184813,0.0006478567,0.0006155064,0.0005884635,0.0002109692,0.01965916,0.125701,0.001254401,0.003242177,0.6283449],"study_design_scores_gemma":[0.00007988329,0.0005881909,0.3028606,0.000126178,0.0005446911,0.001267493,0.0002580166,0.6188276,0.05768258,0.007550061,0.01010577,0.000108855],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5924705,0.005047791,0.3901861,0.0004552583,0.000137533,0.0002829258,0.006034199,0.002522255,0.002863513],"genre_scores_gemma":[0.8114626,0.001144789,0.1790628,0.0001581423,0.0001053329,0.0003559057,0.006458925,0.0001232459,0.001128212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001644502,"threshold_uncertainty_score":0.005048871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938593079518336,"score_gpt":0.4078606339185214,"score_spread":0.368474703123338,"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."}}