ArticleBMC bioinformatics2017
A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- A practical introduction to holo-omics.Cell reports methods · 2024Review
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.PLoS computational biology · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.bioRxiv : the preprint server for biology · 2024Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- Review
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.Cancers · 2022Review
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus.Genome biology · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- TiMEG: an integrative statistical method for partially missing multi-omics data.Scientific reports · 2021Article
- Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma.Molecular genetics and genomics : MGG · 2020Article
- A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.GigaScience · 2019Article
- Review
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundHigh-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking.
resultsIn this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally.
conclusionsThe empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.
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