Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.
LI MinLI QiGANEGODA Gamage UpekshaWANG JianXinWU FangXiangPAN Yi
Parameterized computation is a new method dealing with NP-hard problems, which has attracted a lot of attentions in theoretical computer science. As a practical preprocessing method for NP-hard problems, kernelizaiton in parameterized computation has recently become an active research area. In this paper, we discuss several kernelizaiton techniques, such as crown decomposition, planar graph vertex partition, randomized methods, and kernel lower bounds, which have been used widely in the kernelization of many hard problems.
Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.
Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.
