Imperial College London
Portrait Picture

Dr James Kinross

Faculty of Medicine, Department of Surgery & Cancer

Reader in General Surgery
 
 
 
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Contact

 

+44 (0)20 3312 1947j.kinross

 
 
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Location

 

1029Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Galea:2017:10.1038/s41598-017-14092-7,
author = {Galea, D and Inglese, P and Cammack, L and Strittmatter, N and Rebec, M and Mirnezami, R and Laponogov, I and Kinross, J and Nicholson, J and Takats, Z and Veselkov, KA},
doi = {10.1038/s41598-017-14092-7},
journal = {Scientific Reports},
title = {Translational utility of a hierarchical classification strategy in biomolecular data analytics.},
url = {http://dx.doi.org/10.1038/s41598-017-14092-7},
volume = {7},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.
AU  - Galea,D
AU  - Inglese,P
AU  - Cammack,L
AU  - Strittmatter,N
AU  - Rebec,M
AU  - Mirnezami,R
AU  - Laponogov,I
AU  - Kinross,J
AU  - Nicholson,J
AU  - Takats,Z
AU  - Veselkov,KA
DO  - 10.1038/s41598-017-14092-7
PY  - 2017///
SN  - 2045-2322
TI  - Translational utility of a hierarchical classification strategy in biomolecular data analytics.
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-017-14092-7
UR  - http://hdl.handle.net/10044/1/53085
VL  - 7
ER  -
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