Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions

Aderhold, Andrej; Smith, V. Anne; Husmeier, Dirk

doi:10.1002/9781119078845.ch27

Cited by 2 publications

(3 citation statements)

References 51 publications

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“…Fragmented glyphs become harder to perceive as one, especially when they are located next to other glyphs and occupy a small area. For example, in Figure 4 (c) and (d), the two glyphs between neighbouring cells (1,3) and (1,4) are hard to distinguish. R5: Grid-for any encoding, leave margins between neighbouring matrix cells (i.e., glyphs) or use a salient visual grid.…”

Section: Discussionmentioning

confidence: 99%

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Visual Encodings for Networks with Multiple Edge Types

Vogogias

Archambault

Bach

et al. 2020

Proceedings of the International Conference on Advanced Visual Interfaces

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Figure 1: Examples of visual designs considered for encoding multiple types of edges in matrices. The top row shows an example of a single matrix and the bottom row shows the encoding for each edge type. The encodings use one or more visual variables to represent multiple edges: a) uses a coloured pie chart, b) uses opacity in a pie chart, c) uses a segmented and coloured pie chart d) uses orientation, e) combines position and colour, f) uses size and g) combines size and colour to create a glyph. The designs (d) and (e) were used in our experiments.

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Section: Discussionmentioning

confidence: 99%

“…Our collaborators in computational biology use DBNs to model probabilistic dependencies in complex biological processes. These processes range from gene regulation [45] to brain connectivity [36] and ecological networks [1]. Visualisation and comparison of these networks are crucial, e.g., for comparing edge types and alternative networks.…”

Section: Introductionmentioning

confidence: 99%

Visual Encodings for Networks with Multiple Edge Types

Vogogias

Archambault

Bach

et al. 2020

Proceedings of the International Conference on Advanced Visual Interfaces

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show abstract

“…is method is simple and can effectively handle the time-delay problem. Aderhold et al [19] established the time-delay mutual information among the genes and constructed the multi-time-delay gene regulation network through the dynamic Bayesian network: firstly construct the multi-time-delay mutual information matrix to select the larger gene in the mutual information and then complete the analysis on the gene regulation relationship by the dynamic Bayesian network. Better effect has been obtained by this method.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Zhang

2020

Journal of Healthcare Engineering

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In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlation coefficient, and the mutual information as the similarity measurement indexes for the gene pair, fully reflecting the correlation of gene pairs and simultaneously obtaining the gene regulation relationship and the time delay through the fuzzy label propagation algorithm of the semisupervised learning. Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN can not only correctly select potential regulation genes but also provide details about the gene regulator model, thereby more accurately constructing gene regulation network.

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Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions

Cited by 2 publications

References 51 publications

Visual Encodings for Networks with Multiple Edge Types

Visual Encodings for Networks with Multiple Edge Types

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

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