Symmetry Considerations, Topological Constraints, and Interactions with Physics

Matching gradient descriptors with topological constraints to characterise the crowd dynamics

IET International Conference on Visual Information Engineering (VIE 2006) ◽

10.1049/cp:20060571 ◽

2006 ◽

Cited By ~ 3

Author(s):

B. Zhan ◽

P. Remagnino ◽

S. Velastin ◽

F. Bremond ◽

M. Thonnat

Keyword(s):

Topological Constraints ◽

Crowd Dynamics

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ModularBoost: an efficient network inference algorithm based on module decomposition

BMC Bioinformatics ◽

10.1186/s12859-021-04074-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xinyu Li ◽

Wei Zhang ◽

Jianming Zhang ◽

Guang Li

Keyword(s):

Network Inference ◽

Detection Methods ◽

Inference Problem ◽

Topological Constraints ◽

Inference Algorithms ◽

Module Detection ◽

Series Expression ◽

Gene Modules ◽

Inference Methods ◽

Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

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