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Functional Analysis of Molecular Subtypes with Deep Similarity Learning Model Based on Multi-omics Data

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

The molecular subtypes are crucial for developing personalized treatments. With many biological sequencing data available, integrating multi-Omics data for subtyping cancers has become an attractive research route to explore pathogenesis at the molecular level. Although the biological meaning of molecular subtypes is crucial for explaining the biological mechanism of cancer pathogenesis and designing effective treatment, it is less known. This paper aims to reveal the molecular function of cancer subtypes that are discovered by a deep similarity learning Model based on Muti-omics data. In this paper, we first established molecular subtypes by deep similarity learning Model. Then we detected the differential levels of molecules between subtypes. According to the mapping relationship between the molecule and the gene, the function of the molecule is enriched and analyzed by the corresponding gene. The enriched Gene Ontology (GO) terms and biological pathways reveal the functions of the cancer subtypes. Finally, we estimated our designed workframe on real cancer datasets. Compared to the traditional methods, the deep similarity learning Model achieved a better performance in identifying cancer subtypes. The functional analyses of molecular subtypes provide insights into promoting the development of cancer treatments in the era of precision medicine.

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Acknowledgement

This study was funded in part by the National Natural Science Foundation of China (U1811262, 61802313, 61772426), the Key Research and Development Program of China (2020AAA0108500), the Reformation Research on Education and Teaching at Northwestern Polytechnical University (2021JGY31), the Higher Research Funding on International Talent cultivation at Northwestern Polytechnical University (GJGZZD202202), Research Topic at The Chinese Society of Academic Degrees and Graduate Education (2020ZA1008).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Shuhui Liu, Zhang Yupei & Xuequn Shang

  2. MIIT Big Data Storage and Management Lab, Xi’an, China

    Shuhui Liu, Zhang Yupei & Xuequn Shang

Authors
  1. Shuhui Liu
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  2. Zhang Yupei
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  3. Xuequn Shang
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Corresponding authors

Correspondence to Zhang Yupei or Xuequn Shang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Liu, S., Yupei, Z., Shang, X. (2022). Functional Analysis of Molecular Subtypes with Deep Similarity Learning Model Based on Multi-omics Data. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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