基于随机游走的风险致病基因预测研究进展

风险致病基因预测有助于揭示癌症等复杂疾病发生、发展机理,提高现有复杂疾病检测、预防及治疗水平,为药物设计提供靶标.全基因组关联分析(GWAS)和连锁分析等传统方法通常会产生数百种候选致病基因,采用生物实验方法进一步验证这些候选致病基因往往成本高、费时费力,而通过计算方法预测风险致病基因,并对其进行排序,可有效减少候选致病基因数量,帮助生物学家优化实验验证方案.鉴于目前随机游走算法在风险致病基因预测方面的卓越表现,本文从单元分子网络、多重分子网络和异构分子网络出发,对基于随机游走预测风险致病基因研究进展进行较全面的综述分析,讨论其所存在的计算问题,展望未来可能的研究方向.

Advances in Predicting The Risk Pathogenic Genes With Random Walk

Risk pathogenic genes prediction is important for uncovering the occurrence and development mechanism of complex diseases (i.e., cancer), improving the disease detection, prevention and treatment, and providing the targets for drug design. Traditional gene-mapping approaches, such as linkage analysis and genome-wide association studies (GWAS), often predict hundreds of candidate genes. But it is costly, time-consuming and laborious to further validate these candidate genes with biological experiments. However, the number of candidate pathogenic genes can be effectively reduced by computational and prioritization methods. Considering the excellent performance of random walk with restart (RWR) in predicting the risk pathogenic genes, in this work, we comprehensively discuss the recent progresses of predicting the risk pathogenic genes with RWR from databases related with genes and diseases, the metrics of measuring the similarity between genes/diseases, the strategies of choosing the seed genes of specific disease, and the different genes/diseases network structures. We also point out the computational problems and challenges faced in the process of pathogenic genes prediction.