Disease gene prioritization using network topological analysis from a sequence based human functional linkage network

Abstract

Sequencing large number of candidate disease genes which cause diseases in order to identify the relationship between them is an expensive and time-consuming task. To handle these challenges, different computational approaches have been developed. Based on the observation that genes associated with similar diseases have a higher likelihood of interaction, a large class of these approaches relay on analyzing the topological properties of biological networks. However, the incomplete and noisy nature of biological networks is known as an important challenge in these approaches. In this paper, we propose a two-step framework for disease gene prioritization: (1) construction of a reliable human FLN using sequence information and machine learning techniques, (2) prioritizing the disease gene relations based on the constructed FLN. On our framework, unlike other FLN based frameworks that using FLNs based on integration of various low quality biological data, the sequence of proteins is used as the comprehensive data to construct a reliable initial network. In addition, the physicochemical properties of amino-acids are employed to describe the functionality of proteins. All in all, the proposed approach is evaluated and the results indicate the high efficiency and validity of the FLN in disease gene prioritization.