FSMKL statistics

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Citations per year

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Associated diseases

Associated diseases

FSMKL specifications


Unique identifier OMICS_12546
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data The algorithm can work with one or several datasets. We have probed that most of cases, the inclusion of different datasets increases the accuracy of prediction. Each dataset must be in a table with rows as samples and features as columns.
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes


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  • person_outline José A. Seoane <>

Publication for FSMKL

FSMKL in publication

PMCID: 4713050
PMID: 26758643
DOI: 10.1038/srep19256

[…] accuracy of each of these techniques with proteins extracted from ten 2-de images of different types of tissues and different experimental conditions. we found that the best classification model was fsmkl, a data integration method using multiple kernel learning, which achieved auroc values above 95% while using a reduced number of features. this technique allows us to increment […]

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FSMKL institution(s)
MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Clifton, UK; MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton, UK; Intelligent Systems Laboratory, University of Bristol, Bristol, UK
FSMKL funding source(s)
UK Medical Research Council (G1000427)

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