↓ Skip to main content

A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps

Overview of attention for article published in BMC Bioinformatics, May 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
28 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1657-1
Pubmed ID
Authors

Jing Liu, Yaxiong Chi, Chen Zhu, Yaochu Jin

Abstract

Reconstructing gene regulatory networks (GRNs) from expression data plays an important role in understanding the fundamental cellular processes and revealing the underlying relations among genes. Although many algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle large-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an optimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs have good ability to simulate the observed time series. This is a typical big optimization problem, since the number of variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase in the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically reconstructing large-scale GRNs. In this paper, we use fuzzy cognitive maps (FCMs) to model GRNs, in which each node of FCMs represent a single gene. However, most of the current training algorithms for FCMs are only able to train FCMs with dozens of nodes. Here, a new evolutionary algorithm is proposed to train FCMs, which combines a dynamical multi-agent genetic algorithm (dMAGA) with the decomposition-based model, and termed as dMAGA-FCMD, which is able to deal with large-scale FCMs with up to 500 nodes. Both large-scale synthetic FCMs and the benchmark DREAM4 for reconstructing biological GRNs are used in the experiments to validate the performance of dMAGA-FCMD. The dMAGA-FCMD is compared with the other four algorithms which are all state-of-the-art FCM training algorithms, and the results show that the dMAGA-FCMD performs the best. In addition, the experimental results on FCMs with 500 nodes and DREAM4 project demonstrate that dMAGA-FCMD is capable of effectively and computationally efficiently training large-scale FCMs and GRNs.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Master 5 18%
Researcher 3 11%
Student > Doctoral Student 3 11%
Student > Bachelor 1 4%
Other 1 4%
Unknown 9 32%
Readers by discipline Count As %
Computer Science 8 29%
Engineering 3 11%
Psychology 2 7%
Nursing and Health Professions 1 4%
Agricultural and Biological Sciences 1 4%
Other 4 14%
Unknown 9 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 May 2017.
All research outputs
#20,420,242
of 22,971,207 outputs
Outputs from BMC Bioinformatics
#6,881
of 7,306 outputs
Outputs of similar age
#270,382
of 310,587 outputs
Outputs of similar age from BMC Bioinformatics
#95
of 103 outputs
Altmetric has tracked 22,971,207 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,306 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 310,587 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.