ArticleBioinformatics (Oxford, England)2019
Tightly integrated genomic and epigenomic data mining using tensor decomposition.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders.BMC bioinformatics · 2021Article
- Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction.Scientific reports · 2021Article
- Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging.Frontiers in genetics · 2021Review
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1 author.
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Abstract
Motivation: Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities. Results: In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high-order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression (STR) and ridge tensor regression (RTR). The results demonstrate that the tensor decomposition based approaches can achieve better performance than the models based individual data type and the concatenation approach. Supplementary information: Supplementary data are available at Bioinformatics online.
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