research-article

Cancer Subtype Discovery Based on Integrative Model of Multigenomic Data

Authors:

Chun-Hou ZhengAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 14, Issue 5

Pages 1115 - 1121

https://doi.org/10.1109/TCBB.2016.2621769

Published: 01 September 2017 Publication History

Abstract

One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data DNA methylation, mRNA expression, etc., which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace. In this paper, we develop a dimension-reduction and data-integration method for indentifying cancer subtypes, named Scluster. First, Scluster, respectively, projects the different original data into the principal subspaces by an adaptive sparse reduced-rank regression method. Then, a fused patient-by-patient network is obtained for these subgroups through a scaled exponential similarity kernel method. Finally, candidate cancer subtypes are identified using spectral clustering method. We demonstrate the efficiency of our Scluster method using three cancers by jointly analyzing mRNA expression, miRNA expression, and DNA methylation data. The evaluation results and analyses show that Scluster is effective for predicting survival and identifies novel cancer subtypes of large-scale multi-omics data.

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Cited By

Guan QZhao JZheng C(2021)SNEMO: Spectral Clustering Based on the Neighborhood for Multi-omics DataIntelligent Computing Theories and Application10.1007/978-3-030-84532-2_44(490-498)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84532-2_44
Yuan LShen Z(2021)Joint Association Analysis Method to Predict Genes Related to Liver CancerIntelligent Computing Theories and Application10.1007/978-3-030-84532-2_33(364-373)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84532-2_33

Cancer Subtype Discovery Based on Integrative Model of Multigenomic Data
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 14, Issue 5

September 2017

202 pages

ISSN:1545-5963

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Copyright © 2017.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 September 2017

Published in TCBB Volume 14, Issue 5

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Cited By

Guan QZhao JZheng C(2021)SNEMO: Spectral Clustering Based on the Neighborhood for Multi-omics DataIntelligent Computing Theories and Application10.1007/978-3-030-84532-2_44(490-498)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84532-2_44
Yuan LShen Z(2021)Joint Association Analysis Method to Predict Genes Related to Liver CancerIntelligent Computing Theories and Application10.1007/978-3-030-84532-2_33(364-373)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-84532-2_33

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