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.
翻译:预测基因功能是后基因组时代生物学家面临的一项挑战。基因及其产品之间的相互作用构成可以用来推断基因功能的网络。以前的大多数研究采用了一种联系假设,即假定基因相互作用表明连接基因之间的功能相似性。在本研究中,我们提议使用基因背景图,即与焦点基因有关的基因相互作用网络来推断其功能。在一个以内核为基础的机器学习框架内,我们设计了一个上下文图内核,以捕捉上下文图中的信息。我们对与P53有关的基因的试验床进行的实验研究表明,使用间接基因相互作用的好处,并表明所提议的方法在经验上优于基于联系的计算方法,例如尽量减少不连贯的连接基因和传播内核的算法。