{"id":"W1563388055","doi":"10.1186/1471-2105-5-103","title":"Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays","year":2004,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"Hospital for Sick Children","keywords":"DNA microarray; Normalization (sociology); Computational biology; Gene chip analysis; Microarray; Oligonucleotide; Biology; Gene expression profiling; Computer science; Bioinformatics; Gene expression; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008869875,0.002466563,0.002026587,0.002271075,0.001225243,0.002032897,0.002120594,0.001417131,0.00627162],"category_scores_gemma":[0.02453212,0.001086436,0.001703687,0.00436721,0.001162867,0.001800122,0.001162292,0.002694882,0.005858383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179222,"about_ca_system_score_gemma":0.001338038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031185,"about_ca_topic_score_gemma":0.002281119,"domain_scores_codex":[0.9916488,0.002641161,0.0006174928,0.001459318,0.003334183,0.0002989497],"domain_scores_gemma":[0.9927248,0.003029802,0.0004124331,0.001341241,0.002404328,0.00008733344],"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":[0.0003410655,0.0002548865,0.005640858,0.001448554,0.000188748,0.0001911106,0.000434364,0.005795317,0.7051613,0.004181007,0.01253617,0.2638267],"study_design_scores_gemma":[0.00008026265,0.0005336337,0.02524263,0.0002491395,0.0002672056,0.001240388,0.0002437026,0.07162515,0.8184462,0.01141889,0.07040697,0.0002458423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02289749,0.001177415,0.9658278,0.0003738272,0.0004907246,0.0003792453,0.0006796748,0.006592232,0.001581764],"genre_scores_gemma":[0.04076191,0.00107804,0.9507061,0.0003142221,0.0001296694,0.001202731,0.002127979,0.001725109,0.001954164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008869875,"threshold_uncertainty_score":0.04690892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361467288176512,"score_gpt":0.2318689874051802,"score_spread":0.2182543145234151,"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."}}