Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Xiao, Qianghua; Wang, Jianxin; Peng, Xiaoqing; Wu, Fang‐Xiang

doi:10.1186/1477-5956-11-s1-s20

Cited by 21 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al [ 23 ] have adopted AR (autoregressive) model to analyze the time dependence of time-course (dynamic) gene expression profiles. In [ 26 ], the time-dependent relationships can be modeled by an AR model of order p , denoted by AR ( p ), as follow:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Identifying essential proteins from active PPI networks constructed with dynamic gene expression

et al. 2015

Self Cite

View full text Add to dashboard Cite

Essential proteins are vitally important for cellular survival and development, and identifying essential proteins is very meaningful research work in the post-genome era. Rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality at the network level. A series of centrality measures have been proposed to discover essential proteins based on the PPI networks. However, the PPI data obtained from large scale, high-throughput experiments generally contain false positives. It is insufficient to use original PPI data to identify essential proteins. How to improve the accuracy, has become the focus of identifying essential proteins. In this paper, we proposed a framework for identifying essential proteins from active PPI networks constructed with dynamic gene expression. Firstly, we process the dynamic gene expression profiles by using time-dependent model and time-independent model. Secondly, we construct an active PPI network based on co-expressed genes. Lastly, we apply six classical centrality measures in the active PPI network. For the purpose of comparison, other prediction methods are also performed to identify essential proteins based on the active PPI network. The experimental results on yeast network show that identifying essential proteins based on the active PPI network can improve the performance of centrality measures considerably in terms of the number of identified essential proteins and identification accuracy. At the same time, the results also indicate that most of essential proteins are active.

show abstract

Section: Methodsmentioning

confidence: 99%

“…We then use a threshold function to compute an active threshold for each gene according to their expression data. We finally construct an active PPI network (NF-APIN) [ 26 ]. Our threshold function is described as follows:…”

Section: Methodsmentioning

confidence: 99%

Identifying essential proteins from active PPI networks constructed with dynamic gene expression

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the methods to experimentally discover essential genes in biology are time consuming and inefficient. Consequently, several recent computational methods have been proposed to identify essential genes [4,5]. Generally, these computational methods can be classified into three categories: sequence-based methods, network-based methods, and multi-biological information-based methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein–Protein Interactive Networks

Zhang

Peng

Yang

et al. 2019

Genes

View full text Add to dashboard Cite

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

show abstract

“…Therefore, if the value of a gene’s expression is greater than the threshold, the gene is assumed to be active otherwise, inactive. In more recent methods [ 26 , 36 ] authors proposed a distinct threshold for each gene and called it as active threshold. These active thresholds are based on the mean and the standard deviation of a gene expression levels in all time-points.…”

Section: Introductionmentioning

confidence: 99%

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

Lakizadeh

Jalili

2016

PLoS ONE

View full text Add to dashboard Cite

Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent the inherent dynamicity of protein interactions. Recently, some researchers try to exploit the dynamicity of PPI networks by constructing a set of dynamic PPI subnetworks correspondent to each time-point (column) in a gene expression data. However, many genes can participate in multiple biological processes and cellular processes are not necessarily related to every sample, but they might be relevant only for a subset of samples. So, it is more interesting to explore each subnetwork based on a subset of genes and conditions (i.e., biclusters) in a gene expression data. Here, we present a new method, called BiCAMWI to employ dynamicity in detecting protein complexes. The preprocessing phase of the proposed method is based on a novel genetic algorithm that extracts some sets of genes that are co-regulated under some conditions from input gene expression data. Each extracted gene set is called bicluster. In the detection phase of the proposed method, then, based on the biclusters, some dynamic PPI subnetworks are extracted from input static PPI network. Protein complexes are identified by applying a detection method on each dynamic PPI subnetwork and aggregating the results. Experimental results confirm that BiCAMWI effectively models the dynamicity inherent in static PPI networks and achieves significantly better results than state-of-the-art methods. So, we suggest BiCAMWI as a more reliable method for protein complex detection.

show abstract

Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Cited by 21 publications

References 34 publications

Identifying essential proteins from active PPI networks constructed with dynamic gene expression

Identifying essential proteins from active PPI networks constructed with dynamic gene expression

A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein–Protein Interactive Networks

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

Contact Info

Product

Resources

About