{"id":"W1976245710","doi":"10.1089/cmb.2005.12.1083","title":"The Statistical Analysis of Spatially Clustered Genes under the Maximum Gap Criterion","year":2005,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Alfred P. Sloan Foundation","keywords":"Pairwise comparison; Genome; Comparative genomics; Computational biology; Identification (biology); Genomics; Biology; Set (abstract data type); Statistical model; Cluster (spacecraft); Statistical analysis; Gene; Genetics; Computer science; Mathematics; 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":[0.0003459573,0.00006725915,0.0001378917,0.00007684717,0.00009073144,0.00001589455,0.0002063517,0.00006489211,0.0000347148],"category_scores_gemma":[0.00005103058,0.0000365031,0.0001209457,0.0001335086,0.0001484895,0.000003218186,0.00003897088,0.00006888269,0.000001448658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001335314,"about_ca_system_score_gemma":0.0001224693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002914534,"about_ca_topic_score_gemma":0.00002705567,"domain_scores_codex":[0.9990785,0.0001966612,0.0004055528,0.00009552956,0.0001326671,0.0000910755],"domain_scores_gemma":[0.9990022,0.0001533447,0.0003611483,0.0001213219,0.0003274017,0.00003453622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001095263,0.0001760452,0.004447415,0.000008417168,0.001952686,0.000001089027,0.0001329229,0.3612638,0.4539171,0.01308654,0.006383328,0.1575354],"study_design_scores_gemma":[0.002926456,0.001945678,0.4788299,0.00002685486,0.001308268,0.0001509736,0.0007380675,0.06069857,0.04371827,0.0352324,0.3739228,0.0005017953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5492321,0.002001076,0.4424839,0.005768789,0.000264013,0.00007005336,0.00003041906,0.000001804908,0.0001478529],"genre_scores_gemma":[0.9958064,0.0002268436,0.003096083,0.0004988461,0.0002473527,0.000002109883,0.00006936116,0.000004167922,0.0000488435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4743824,"threshold_uncertainty_score":0.1488553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02096222089491316,"score_gpt":0.319773445089963,"score_spread":0.2988112241950499,"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."}}