Label reusing based graph neural network for unbalanced classification of personalized driver genes in cancer
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- @Article{DBLP:journals/asc/WanWZCBWZLG24,
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author = "Han-Wen Wan and Meng-Han Wu and Wen-Shan Zhao and
Han Cheng and Ying Bi and Xian-Fang Wang and
Xiang-Rui Zhang and Yan Li and Wei-Feng Guo",
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title = "Label reusing based graph neural network for
unbalanced classification of personalized driver genes
in cancer",
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journal = "Applied Soft Computing",
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year = "2024",
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volume = "159",
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number = "C",
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pages = "111658",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Personalized
driver genes, Graph attention neural network, Label
reuse, Semi-supervised learning, Tumor heterogeneity",
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publisher = "Elsevier Science Publishers B. V.",
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ISSN = "1568-4946",
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timestamp = "Fri, 31 May 2024 21:05:36 +0200",
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biburl = "https://dblp.org/rec/journals/asc/WanWZCBWZLG24.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.1016/j.asoc.2024.111658",
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DOI = "doi:10.1016/J.ASOC.2024.111658",
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size = "15 pages",
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abstract = "It is a big challenge to identify personalized driver
genes (PDGs) for understanding tumor heterogeneity of
cancer individual patients. From the perspective of
machine learning, identifying PDGs is an inherent class
imbalance issue due to the fewer known driver genes
than most passenger ones. However, existing machine
learning based methods including unsupervised and
supervised learning based methods ignore the importance
of limited well-established cancer tissue specific
driver genes(CSDGs) for this class imbalance issue.
Here we converted the PDG prediction issue to a
semi-supervised classification task and a novel method
(namely PersonalizedGNN) was developed to identify PDGs
by using graph attention neural network and label reuse
strategy in personalized gene interaction network
(PGIN). PersonalizedGNN effectively utilizes the
structure information of PGIN and the limited
well-established CSDG information for achieving
promising performance. Using the breast cancer and lung
cancer datasets from The Cancer Genome Atlas, we
validated our method and compared it with other
advanced methods. PersonalizedGNN showed outstanding
potential in identifying cancer driver genes in terms
of prediction precision. Furthermore, we could discover
subtype-specific de novo cancer driver genes and in
vitro cell-based assays for a novel driver gene FZD7 in
lung squamous cell carcinoma cells further validated
the PersonalizedGNN. In summary, PersonalizedGNN offers
a new effective perspective for discovering PDGs by
considering information of prior known CSDGs in PGIN
which help researchers understand tumor heterogeneity
of cancer individual patients.",
- }
Genetic Programming entries for
Han-Wen Wan
Meng-Han Wu
Wen-Shan Zhao
Han Cheng
Ying Bi
Xian-Fang Wang
Xiang-Rui Zhang
Yan Li
Wei-Feng Guo
Citations