非负矩阵算法在遗传相互作用数据中的应用

Non-Negative Matrix Factorization in the Genetic Interaction Data

Epistatic miniarrary profile(EMAP)在多种模式生物中的研究产生了许多高通量数据和遗传相互作用网络.但是怎样探究这些数据中的有效生物学信息是现在非常关键的问题.本文我们采用非负矩阵分解的算法对遗传相互作用数据进行聚类分析,从而发现其中隐藏的功能模块,分析基因功能关系等.该方法能有效的避免传统聚类算法的诸多局限性.

Epistatic miniarrary profile （EMAP） has been used in many model organisms, resulting in a mount of high throughout data and the genetic interaction networks. But how to exploit meaningful information under these data efficiently is one important problem. In this paper, we propose the non-negative matrix factorization （NMF） to cluster the genetic interaction data, in order to find the hidden functional modular and then analysis the relationships among different genes.