{"id":"W1865766120","doi":"10.1186/gb-2001-2-8-software0001","title":"AFM 4.0: a toolbox for DNA microarray analysis","year":2001,"lang":"en","type":"article","venue":"Genome biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Atomic force microscopy; Software; Toolbox; Microarray analysis techniques; Venn diagram; Microarray; Microarray databases; Biology; Computer science; Cluster analysis; DNA microarray; Gene chip analysis; Protein microarray; Computational biology; Data mining; Bioinformatics; Database; Nanotechnology; Genetics; Operating system; Materials science; Gene; Gene expression; Programming language; Artificial intelligence; Mathematics","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":[0.0001227087,0.0001135473,0.0001568502,0.0001126718,0.00007319544,0.00001132616,0.0001889668,0.0001783966,0.0001213075],"category_scores_gemma":[0.00002915167,0.0001015134,0.0001763252,0.0002368145,0.00005324596,0.000001317204,0.00003374397,0.00003020668,0.00001969225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001418808,"about_ca_system_score_gemma":0.00004168756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006001136,"about_ca_topic_score_gemma":0.00002803853,"domain_scores_codex":[0.9991132,0.00004275903,0.0001703205,0.000402447,0.00003030772,0.0002409786],"domain_scores_gemma":[0.9994094,0.000007785142,0.00007619735,0.0003683387,0.000072815,0.00006549736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009017226,0.00002503973,0.005149445,0.000002813873,0.0001848167,2.30916e-7,0.00002119322,0.00001982267,0.9904891,0.0001472242,0.001296698,0.002573489],"study_design_scores_gemma":[0.0003954514,0.0001845478,0.01445087,6.445327e-7,0.00009176505,0.000004386207,0.00005244735,0.00001603392,0.07880058,0.0001385679,0.9057021,0.0001626071],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9042886,0.001923982,0.08971349,0.0007107929,0.0002628318,0.0002983302,0.00007935491,0.00002422794,0.00269836],"genre_scores_gemma":[0.993139,0.000270637,0.001145336,0.0006267091,0.0003622888,0.0001172895,0.0009285509,0.00001286775,0.003397297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9116884,"threshold_uncertainty_score":0.4139597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170578972120617,"score_gpt":0.2801671530034108,"score_spread":0.2631092557913491,"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."}}