Abstract

AbstractAlzheimer's disease (AD) is characterized by a complex pathogenesis, limited diagnostic methods, and a lack of effective therapeutic drugs in clinical settings, posing significant challenges in modern medical research. Bioinformatics offers new perspectives for identifying key pathological biomarkers of AD, analyzing differentially expressed genes in AD, screening for effective drug targets against AD, studying the mechanisms of AD pathogenesis, and discovering novel anti-AD drugs. However, data preprocessing and statistical analysis methods in bioinformatics research can significantly impact results, and there is a lack of consistency and coordination in analysis methods across platforms and laboratories in practical studies, making it difficult to compare data between studies. Therefore, it is crucial to establish standardized operating procedures and quality control protocols, improve the reproducibility of methods across platforms, and promote data comparison between studies.

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