论文标题
基因函数与基因相互作用网络的预测：一个上下文图核方法
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
论文作者
论文摘要
在基因组时代，预测基因功能是生物学家的挑战。基因及其产品之间的相互作用组成可用于推断基因功能的网络。大多数先前的研究都采用了连锁假设，即，他们认为基因相互作用表明连接基因之间的功能相似性。在这项研究中，我们建议使用基因的上下文图，即与焦点基因相关的基因相互作用网络来推断其功能。在基于内核的机器学习框架中，我们设计了一个上下文图表，以在上下文图中捕获信息。我们对p53相关基因测试床的实验研究表明，使用间接基因相互作用的优点，并显示了所提出的方法比基于链接促进的方法的经验优势，例如算法以最大程度地减少连接的基因和扩散核的不一致。
Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.
