Abstract：
The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes. Conventional methods cannot be used to investigate many rare or lethal diseases. Monitoring gene expression values in different samples using microarray technology is one of the best and most accurate ways to identify disease-causing genes. One of the most recent advances in experimental molecular biology is microarrays, which allow researchers to simultaneously monitor the expression levels of thousands of genes. Statistical analysis of microarray data might aid gene discovery by revealing pathways related to the target gene and facilitating identification of candidate genes. Systems biology, an interdisciplinary approach, has emerged as a crucial analytic tool with the potential to reveal previously unidentified causes and consequences of human illness. Genetic, environmental, immunological, or neurological factors have been implicated in the developing complex disorders like cancer. Because of this, it is important to approach the study of such disease from a novel perspective. The system biology approach allows us to rapidly identify disease-causing genes and assess their viability as therapeutic targets. This chapter demonstrates systems biology approaches to identify candidate genes using public database. Oral squamous cell carcinoma (OSCC) is used as a model disease to show how systems biology can be used successfully to identify and prioritize disease genes.
摘要：
发现潜在的致病基因可以帮助医学进步。后基因组时代使这项任务变得更加困难。现代高通量方法尚未解决识别疾病基因的问题。常规方法不能用于研究许多罕见或致命疾病。使用微阵列技术监测不同样品中的基因表达值是鉴定致病基因的最佳和最准确的方法之一。实验分子生物学的最新进展之一是微阵列，这使得研究人员能够同时监测数千个基因的表达水平。微阵列数据的统计分析可以通过揭示与靶基因相关的途径和促进候选基因的鉴定来帮助基因发现。系统生物学,跨学科的方法，已经成为一种至关重要的分析工具，有可能揭示以前未知的人类疾病的原因和后果。遗传,环境,免疫学，或神经因素与癌症等复杂疾病的发展有关。正因为如此,从新的角度研究这种疾病是很重要的。系统生物学方法使我们能够快速识别致病基因并评估其作为治疗靶标的可行性。本章演示了使用公共数据库识别候选基因的系统生物学方法。口腔鳞状细胞癌（OSCC）被用作模型疾病，以显示如何成功地使用系统生物学来识别和优先考虑疾病基因。
