Cited by 44Bioinformatics and Genomic NetworksGene Regulatory Network AnalysisCancer Genomics and Diagnostics

Network control principles for identifying personalized driver genes in cancer

Guo, Wei-Feng, Zhang, Shao-Wu, Zeng, Tao, Akutsu, Tatsuya, Chen, Luonan·2020·Briefings in Bioinformatics

Abstract

To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype–phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.

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Crossref · 10.1093/bib/bbz089

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  • Graph representation learning has been successful in characterizing drugs at the systems level without patient data to predict interactions with other drugs, protein targets, side effects, and diseases.

    “Graph representation learning has been successful in characterizing drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects, diseases [6, 38, 39, 40, 48, 165].”

    From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [165]

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Authors
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L.
Affiliations
  • Northwestern Polytechnical University (CN)
  • Chinese Academy of Sciences (CN)
  • Kyoto University Institute for Chemical Research (JP)
Corresponding author
  • Zhang, Shao-Wu
  • Zeng, Tao
  • Chen, Luonan
Venue
Briefings in Bioinformatics
Published
2019-11-10
Volume
21
Issue
5
Pages
1641-1662
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W2989260018
Cited by
44
References
178
Citations by year
2026: 2 · 2025: 7 · 2024: 8 · 2023: 11 · 2022: 8 · 2021: 5 · 2020: 3
FWCI
0.35
Type
journal article
Language
en
Open access
closed
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)
  • Natural Science Foundation of Shanghai (17ZR1446100)
  • Strategic Priority Research Program of the Chinese Academy of Sciences (XDB13040700)
  • National Key R&D Program (2016YFC0903400, 2017YFA0505500)
  • National Natural Science Foundation of China (11871456, 81471047, 31771476, 91430111, 61473232, 61873202)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
cancer driver genes, network control theory, tumor heterogeneity, oncogene activation, genotype-phenotype correlation, tumor progression, TCGA cancer datasets
MeSH terms
Algorithms, Humans, Mutation, Neoplasms, Genetic Heterogeneity, Computational Biology

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