The identification of disease genes is the crucial step in uncovering disease pathology and systematically analyzing polygenetic disease. The high-throughput technology has advanced the development of network-based approaches for disease gene prediction. Based on the "guilt-by-association" principle, now disease gene prioritization methods can measure the proximity between candidate genes and causal genes so as to pinpoint the potential disease genes. In this review, we first classify the network-based approaches for disease gene prediction into three categories: the approach based on disease genes information, the approach integrated with phenotype similarity and the approach that integrates several results from multiple data resources into one final result. Then we bring out the current situation of these approaches and summarize the current achievements and existing problems. Finally we put forward some suggestions for future research.%疾病基因预测是揭示疾病作用机理、系统研究复杂疾病的关键环节.高通量生物实验技术的成熟,促进了基于分子网络的疾病基因预测方法的发展.基于"连接有罪"的生物学假设,疾病基因预测算法在生物网络中衡量候选基因与已知疾病基因的邻近性或相似性,以预测潜在的致病基因.该文将疾病基因预测方法归纳为3种:基于已知疾病基因信息的预测方法、融合表型相似性信息的预测方法以及融合多结果的预测方法,并对这3种方法的研究现状进行了综述,指出了现有研究成果的不足以及未来的研究方向.
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