ArticleBMC genomics2011
Sublineage structure analysis of Mycobacterium tuberculosis complex strains using multiple-biomarker tensors.
Article in BMC genomics, 2011. 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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Who cites it
7 citing papers in PubMed.
- Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective.bioRxiv : the preprint server for biology · 2026Article
- Article
- Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy.Medical & biological engineering & computing · 2021Article
- Tensor factorization toward precision medicine.Briefings in bioinformatics · 2017Article
- Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Predicting Mycobacterium tuberculosis complex clades using knowledge-based Bayesian networks.BioMed research international · 2014Article
- Data-driven insights into deletions of Mycobacterium tuberculosis complex chromosomal DR region using spoligoforests.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2011Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundStrains of Mycobacterium tuberculosis complex (MTBC) can be classified into major lineages based on their genotype. Further subdivision of major lineages into sublineages requires multiple biomarkers along with methods to combine and analyze multiple sources of information in one unsupervised learning model. Typically, spacer oligonucleotide type (spoligotype) and mycobacterial interspersed repetitive units (MIRU) are used for TB genotyping and surveillance. Here, we examine the sublineage structure of MTBC strains with multiple biomarkers simultaneously, by employing a tensor clustering framework (TCF) on multiple-biomarker tensors.
resultsSimultaneous analysis of the spoligotype and MIRU type of strains using TCF on multiple-biomarker tensors leads to coherent sublineages of major lineages with clear and distinctive spoligotype and MIRU signatures. Comparison of tensor sublineages with SpolDB4 families either supports tensor sublineages, or suggests subdivision or merging of SpolDB4 families. High prediction accuracy of major lineage classification with supervised tensor learning on multiple-biomarker tensors validates our unsupervised analysis of sublineages on multiple-biomarker tensors.
conclusionsTCF on multiple-biomarker tensors achieves simultaneous analysis of multiple biomarkers and suggest a new putative sublineage structure for each major lineage. Analysis of multiple-biomarker tensors gives insight into the sublineage structure of MTBC at the genomic level.
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