Name
Title | ||
|---|---|---|
Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps |
Abstract | ||
|---|---|---|
Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEA
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FCM</sub>
-GRN, to reconstruct GRNs based on FCMs. In EMOEA
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FCM</sub>
-GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEA
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FCM</sub>
-GRN is efficient and able to reconstruct GRNs accurately. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/trit.2018.1059 | CAAI Transactions on Intelligence Technology |
Keywords | Field | DocType |
genetics,fuzzy set theory,evolutionary computation,biology computing,genomics,learning (artificial intelligence),optimisation | Training set,Evolutionary algorithm,Computer science,Fuzzy cognitive map,Multi-objective optimization,Artificial intelligence,Data sequences,Throughput,Gene regulatory network,Machine learning,Network structure | Journal |
Volume | Issue | ISSN |
4 | 1 | 2468-6557 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Liu | 1 | 1043 | 115.54 |
| Yaxiong Chi | 2 | 4 | 0.72 |
| Zongdong Liu | 3 | 1 | 0.35 |
| Shan He | 4 | 478 | 38.07 |