{"id":"W3048579307","doi":"","title":"Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning","year":2020,"lang":"en","type":"article","venue":"Memorial University Research Repository (Memorial University)","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ulcerative colitis; Feature selection; Support vector machine; Artificial intelligence; Classifier (UML); Inflammatory bowel disease; Computer science; Machine learning; Colitis; Feature (linguistics); Biomarker discovery; Medicine; Pattern recognition (psychology); Disease; Internal medicine; Gene; Biology; Proteomics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003422617,0.0002377817,0.0002407952,0.0002306021,0.001736303,0.0001221714,0.0003685509,0.0003195952,0.00002078363],"category_scores_gemma":[0.0004066216,0.0003068381,0.000124018,0.000575061,0.0002755821,0.00004204062,0.0006239924,0.0006819088,0.000005266193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000390994,"about_ca_system_score_gemma":0.0005196189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001353008,"about_ca_topic_score_gemma":0.0006254267,"domain_scores_codex":[0.9967158,0.001439111,0.0001302088,0.0007933546,0.0004493726,0.000472173],"domain_scores_gemma":[0.9986452,0.0001186356,0.0001274529,0.0001944283,0.0004629669,0.0004513007],"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":[0.03097465,0.0001137495,0.01483049,0.00008517266,0.0003939126,0.002877653,0.001406898,0.008437376,0.9392548,0.000449561,0.0007320623,0.0004437492],"study_design_scores_gemma":[0.02732212,0.004162565,0.04525315,0.0002446795,0.0009291705,0.0001463696,0.01917957,0.09156533,0.4948826,0.00005507358,0.3132311,0.003028268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961199,0.00003298999,0.0009768861,0.00007979278,0.0007806862,0.0004499262,0.00007943316,0.00006781959,0.001412633],"genre_scores_gemma":[0.9950563,0.00003285047,0.0004708313,0.00001363496,0.003119011,2.378116e-7,0.00007748829,0.00002717266,0.001202438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4443721,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250462393695452,"score_gpt":0.2434079665541631,"score_spread":0.2183617271846179,"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."}}