ArticleGenes2018
A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics.Heliyon · 2024Article
- Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.Frontiers in cell and developmental biology · 2024Review
- Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer.Scientific reports · 2023Article
- A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures.Methods in molecular biology (Clifton, N.J.) · 2023Article
- A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data.BMC bioinformatics · 2021Article
- Article
- A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.BMC bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of cancer subtypes is crucial to cancer diagnosis and treatments. A number of methods have been proposed to identify cancer subtypes by integrating multi-omics data in recent years. However, the existing methods rarely consider the biases of similarity between samples and weights of different omics data in integration. More accurate and flexible integration approaches need to be developed to comprehensively investigate cancer subtypes. In this paper, we propose a simple and flexible similarity fusion model for integrating multi-omics data to identify cancer subtypes. We consider the similarity biases between samples in each omics data and predict corrected similarities between samples using a generalized linear model. We integrate the corrected similarity information from multi-omics data according to different data-view weights. Based on the integrative similarity information, we cluster patient samples into different subtype groups. Comprehensive experiments demonstrate that the proposed approach obtains more significant results than the state-of-the-art integrative methods. In conclusion, our approach provides an effective and flexible tool to investigate subtypes in cancer by integrating multi-omics data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.