Classification of Autism Genes using Network Science and Linear Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Zhang:2020:EuroGPa,
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author = "Yu Zhang2 and Yuanzhu Chen and Ting Hu",
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title = "Classification of Autism Genes using Network Science
and Linear Genetic Programming",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "279--294",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Linear
genetic programming, Autism spectrum disorders, Human
molecular interaction network, Complex networks,
Disease-gene association",
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isbn13 = "978-3-030-44093-0",
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DOI = "doi:10.1007/978-3-030-44094-7_18",
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abstract = "Understanding the genetic background of complex
diseases and disorders plays an essential role in the
promising precision medicine. Deciphering what genes
are associated with a specific disease/disorder helps
better diagnose and treat it, and may even prevent it
if predicted accurately and acted on effectively at
early stages. The evaluation of candidate
disease-associated genes, however, requires
time-consuming and expensive experiments given the
large number of possibilities. Due to such challenges,
computational methods have seen increasing applications
in predicting gene-disease associations. Given the
intertwined relationships of molecules in human cells,
genes and their products can be considered to form a
complex molecular interaction network. Such a network
can be used to find candidate genes that share similar
network properties with known disease-associated genes.
In this research, we investigate autism spectrum
disorders and propose a linear genetic programming
algorithm for autism gene prediction using a human
molecular interaction network and known autism-genes
for training. We select an initial set of network
properties as features and our LGP algorithm is able to
find the most relevant features while evolving accurate
predictive models. Our research demonstrates the
powerful and flexible learning abilities of GP on
tackling a significant biomedical problem, and is
expected to inspire further exploration of wide GP
applications.",
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notes = "Linear GP implementation from Ting Hu
See also MSc
https://research.library.mun.ca/14305/
http://www.evostar.org/2020/cfp_eurogp.php Part of
\cite{Hu:2020:GP} EuroGP'2020 held in conjunction with
EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
- }
Genetic Programming entries for
Yu Zhang2
Yuanzhu Chen
Ting Hu
Citations