ArticleBMC medical genomics2018
Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network.Frontiers in immunology · 2022Article
- Novel cancer subtyping method based on patient-specific gene regulatory network.Scientific reports · 2021Article
- The Role of Network Science in Glioblastoma.Cancers · 2021Review
- Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data.Frontiers in genetics · 2020Article
- Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data.Frontiers in genetics · 2019Article
- CEPICS: A Comparison and Evaluation Platform for Integration Methods in Cancer Subtyping.Frontiers in genetics · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genome features and the contribution weights of different data-views in data integration. It is widely accepted that the regulatory associations between features play important roles in cancer subtype studies. In addition, different data-views may have different contributions in data integration for cancer subtype prediction.
resultsIn this paper, we propose a method, CSPRV, to improve the cancer subtype prediction by incorporating multi-sources transcriptome expression data and heterogeneous biological networks. We extract multiple expression features of each genome element based on the regulatory associations in the heterogeneous biological networks and use a generalized matrix correlation method (RV
conclusionsThe consideration of regulatory associations between biological features and data-views contribution is important to improve the understanding of cancer subtypes. The proposed method provides an open framework to incorporate transcriptome expression data and biological regulation network to predict cancer subtypes.
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Registered trials
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