{"id":"W2097449851","doi":"10.1038/nbt1186","title":"Statistical practice in high-throughput screening data analysis","year":2006,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":726,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"","keywords":"Replicate; Computer science; False discovery rate; Identification (biology); Data mining; Preprocessor; Drug discovery; Throughput; Multiple comparisons problem; Machine learning; Artificial intelligence; Bioinformatics; Statistics; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478781202024359,"score_gpt":0.3463605281296443,"score_spread":0.3315727161094007,"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."}}