FSMKL statistics

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Citations per year

Number of citations per year for the bioinformatics software tool FSMKL
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FSMKL specifications

Information


Unique identifier OMICS_12546
Name FSMKL
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

Versioning


No version available

Documentation


Maintainer


  • person_outline José A. Seoane

Publication for FSMKL

FSMKL citations

 (2)
library_books

More Is Better: Recent Progress in Multi Omics Data Integration Methods

2017
Front Genet
PMCID: 5472696
PMID: 28670325
DOI: 10.3389/fgene.2017.00084

[…] exploit unlabeled data. SDP/SVM is a prototype work for kernel-based data integration methods (published in 2004) and doesn't include a programming package.Feature Selection Multiple Kernel Learning (FSMKL) is another method implementing the multiple kernel learning-based supervised learning (Seoane et al., ). This new scheme uses the statistical score for feature selection per data type per pathw […]

library_books

Texture analysis in gel electrophoresis images using an integrative kernel based approach

2016
Sci Rep
PMCID: 4713050
PMID: 26758643
DOI: 10.1038/srep19256

[…] dded (the feature subset search is built into the model construction) methods are the three main approaches. In this study we perform experiments with two filter (MKL filtering groups of features and FSMKL), two wrapper (PSO and GA with an SVM as decision function) and one embedded method (SVM-RFE) methods.A kernel is a function that maps the input into a higher dimension in order to find a new sp […]


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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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