Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network

Guo, Zheng; Li, Yongjin; Gong, Xue; Yao, Chen; Ma, Wencai; Wang, Dong; Li, Yanhui; Zhu, Jing; Zhang, Min; Yang, Da; Wang, Jing

doi:10.1093/bioinformatics/btm294

Cited by 123 publications

(81 citation statements)

References 52 publications

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“…For the identification of differentially expressed subnetworks with respect to GAL80 deletion in yeast, Ideker et al (2002) propose a method that is based on searching for connected subgraphs with high aggregate significance of individual differential expression. Variants of this method are shown to be effective in identifying multiple genetic markers in prostate cancer (Guo et al, 2007), melanoma (Nacu et al, 2007), diabetes , and others (Cabusora et al, 2005;Patil and Nielsen, 2005;Scott et al, 2005).…”

Section: Network-based Approaches To Identification Of Multiple Markersmentioning

confidence: 99%

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Chowdhury

Nibbe

Chance

et al. 2011

Journal of Computational Biology

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Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize fast heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than existing algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane outperform those identified by additive algorithms in predicting metastasis of colorectal cancer (CRC).

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Section: Network-based Approaches To Identification Of Multiple Markersmentioning

confidence: 99%

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Chowdhury

Nibbe

Chance

et al. 2011

Journal of Computational Biology

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“…Our strategy is to identify maximally scored connected View Article Online subnetworks in a weighted graph which is a well-known NP-hard problem. 15,21 Here we use an edge expansion algorithm to detect the significantly dysfunctional pathway which grows in a greedy strategy from an edge with a high score. Fig.…”

Section: Identifying Dysregulated Pathways and Categorizing Edgesmentioning

confidence: 99%

Detecting and analyzing differentially activated pathways in brain regions of Alzheimer's disease patients

et al. 2011

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Alzheimer's disease (AD) generally results in neuronal loss due to protein dysfunction in various brain regions. Genome-wide data have provided new opportunities to analyze the underlying mechanisms of AD. Here, we present a novel network-based systems biology framework to identify and analyze differentially activated pathways by integrating human protein-protein interaction data and gene expression profile data in six brain regions. Specifically, we propose a new scoring system by ranking the edges associated with AD. Then, an edge expansion algorithm is designed to identify the dysfunctional pathways implicated in AD pathogenesis in six brain regions respectively. The analyses of the similarities and differences of these dysfunctional pathways provide insights into understanding the dynamics of AD progression in six brain regions from a network perspective, which will further shed light on the pathogenesis of AD.

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“…Constraint (3) means that there are R flow units entering into the network through the source node v i0 and constraint (4) indicates that there are R edges in the connected subgraph; Constraints (5) and (6) indicate that if there are units entering into e sj , then y sj must be equal to 1, otherwise, y sj must be equal to 0; that is, e sj is selected in the connected subgraph if and only if there are units entering into e sj . Constraint (7) implies that there is only one direction for the flow to enter into e sj . Constraint (8) indicates that the connected subgraph includes the fixed edge (v i0 , v j0 ).…”

Section: The Mecg and 1-cmecg Problemmentioning

confidence: 99%

“…The k-CMECG problem is the CMECG problem with a constraint of having k predetermined edges included in the solution. This problem has potential applications in communication network design, facility expansion and active subnetwork extraction in biological networks [7,8] .…”

Section: Introductionmentioning

confidence: 99%

Exploring the constrained maximum edge-weight connected graph problem

Zhang

et al. 2009

Acta Math. Appl. Sin. Engl. Ser.

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Given an edge weighted graph, the maximum edge-weight connected graph (MECG) is a connected subgraph with a given number of edges and the maximal weight sum. Here we study a special case, i.e. the Constrained Maximum Edge-Weight Connected Graph problem (CMECG), which is an MECG whose candidate subgraphs must include a given set of k edges, then also called the k-CMECG. We formulate the k-CMECG into an integer linear programming model based on the network flow problem. The k-CMECG is proved to be NP-hard. For the special case 1-CMECG, we propose an exact algorithm and a heuristic algorithm respectively. We also propose a heuristic algorithm for the k-CMECG problem. Some simulations have been done to analyze the quality of these algorithms. Moreover, we show that the algorithm for 1-CMECG problem can lead to the solution of the general MECG problem.

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Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 123 publications

References 52 publications

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Detecting and analyzing differentially activated pathways in brain regions of Alzheimer's disease patients

Exploring the constrained maximum edge-weight connected graph problem

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