Influence maximization in graph-based OLAP (GOLAP)

Background: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of cooccurring genes that are the most influential based on an influence maximization algorithm. Results: The sets of co-occurring genes that are the most influential that we discover include RARA-CRBP1, CASP3-BCL2, BCL2-CASP3-CRBP1, RARA-CASP3-CRBP1, FOXJ1-RASSF3-ESR1, FOXJ1-RASSF1A-ESR1, FOXJ1-RASS F1A-TNFAIP8-ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

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“…The following heuristic algorithm is for the IM-SIP problem which is NP-complete [ 33 ]. The heuristic algorithm is greedy.…”

Section: Methodsmentioning

confidence: 99%

“…The correctness and error bounds of the heuristic are proved in [ 33 ]. In the past, some studies apply network analysis methods to gene regulatory networks to solve the influence maximization problem or develop gene regulatory networks.…”

Section: Methodsmentioning

confidence: 99%

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Wang

Jin

Chang

et al. 2020

BMC Med Inform Decis Mak

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“…It should be note that Jin [34] et al present a comprehensive approach to managing influence networks using extended graph models. They discuss a method to find a Strongest Influence Path (SIP) from a source node to a target node.…”

Section: Golapmentioning

confidence: 99%

Diffusion-Based Influence Maximization in GOLAP

Kim

Shih

Sheu

2021

Int. J. Semantic Computing

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Influence analysis is one of the most important research in social network. Specifically, more and more researchers and advertisers are interested in the area of influence maximization (IM). The concept of influence among people or organizations has been the core basis for making business decisions as well as performing everyday social activities. In this research, we begin by extending a new influence diffusion model information diffusion model (IDM) using various constraints. We incorporate colors and additional nodes constraints. By adding colors and constraints for different types of nodes in a graph, we would be able to answer complex queries on multi-dimensional graphs such as ‘find at most two most important genes that are related to lung disease and heart disease’. More specifically, we discuss the following variations of IM-IDM; Colorblind IM-IDM, Colored IM-IDM and Colored IM-IDM with constraints. We also present our experiment results to prove the effectiveness of our model and algorithms.

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“…Figure 1 is the MSNs model. Influence Maximization (IM) [8][9][10][11][12][13][14][15][16] problem is proposed for the study of social networks, and it comes from marketing of economics. Using social network method to analyze the social relations of mobile users in the network can further improve the efficiency of information transmission and forwarding.…”

Section: Introductionmentioning

confidence: 99%

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Gao¹,

Xu²,

Wang³

et al. 2020

Preprint

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Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

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Influence maximization in graph-based OLAP (GOLAP)

Cited by 5 publications

References 80 publications

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Diffusion-Based Influence Maximization in GOLAP

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

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