Learning Asynchronous Boolean Networks From Single-Cell Data Using Multiobjective Cooperative Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8194
- @Article{Shuhua_Gao:Cybernetics,
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author = "Shuhua Gao and Changkai Sun and Cheng Xiang and
Kairong Qin and Tong Heng Lee",
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title = "Learning Asynchronous Boolean Networks From
Single-Cell Data Using Multiobjective Cooperative
Genetic Programming",
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journal = "IEEE Transactions on Cybernetics",
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year = "2022",
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volume = "52",
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number = "5",
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pages = "2916--2930",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2168-2275",
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DOI = "doi:10.1109/TCYB.2020.3022430",
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abstract = "Recent advances in high-throughput single-cell
technologies provide new opportunities for
computational modeling of gene regulatory networks
(GRNs) with an unprecedented amount of gene expression
data. Current studies on the Boolean network (BN)
modeling of GRNs mostly depend on bulk time-series data
and focus on the synchronous update scheme due to its
computational simplicity and tractability. However,
such synchrony is a strong and rarely biologically
realistic assumption. In this study, we adopt the
asynchronous update scheme instead and propose a novel
framework called SgpNet to infer asynchronous BNs from
single-cell data by formulating it into a
multiobjective optimization problem. SgpNet aims to
find BNs that can match the asynchronous state
transition graph (STG) extracted from single-cell data
and retain the sparsity of GRNs. To search the huge
solution space efficiently, we encode each Boolean
function as a tree in genetic programming and evolve
all functions of a network simultaneously via
cooperative coevolution. Besides, we develop a
regulator preselection strategy in view of GRN sparsity
to further enhance learning efficiency. An error
threshold estimation heuristic is also proposed to ease
tedious parameter tuning. SgpNet is compared with the
state-of-the-art method on both synthetic data and
experimental single-cell data. Results show that SgpNet
achieves comparable inference accuracy, while it has
far fewer parameters and eliminates artificial
restrictions on the Boolean function structures.
Furthermore, SgpNet can potentially scale to large
networks via straightforward parallelization on
multiple cores.",
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notes = "Also known as \cite{9216610}",
- }
Genetic Programming entries for
Shuhua Gao
Changkai Sun
Cheng Xiang
Kairong Qin
Tong Heng Lee
Citations