Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
Methods Mol Biol. 2022;2401:51-68. doi: 10.1007/978-1-0716-1839-4_5.
Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.
基因表达微阵列是分子生物学中应用最广泛的高通量技术之一,可用于识别疾病机制和开发诊断及预后基因特征等。然而,由于微阵列分析并未考虑基因、其产物以及整体信号和调控级联之间的复杂关系,这些任务的成功往往受到限制。将蛋白质-蛋白质相互作用数据纳入微阵列分析有助于解决这些挑战。本章综述了蛋白质-蛋白质相互作用如何帮助微阵列分析,从而带来更好地解释疾病机制、更完整的基因注释、更优地为未来实验挑选基因以及更广泛地推广至新数据的基因特征等益处。