SCoRS specifications

Information


Unique identifier OMICS_25893
Name SCoRS
Alternative name Survival Count on Random Subsamples
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Publication for Survival Count on Random Subsamples

SCoRS citation

library_books

Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy

2016
PMCID: 4825072
PMID: 27055815
DOI: 10.1186/s12916-016-0609-3

[…] ical variables were defined using machine learning methods. Time-to-positivity was modelled with Xq and other clinical variables with Xd. Firstly, stability selection [], implemented according to the SCoRS framework, was used for feature selection []. A total of 500 sub-sampling iterations were performed with a sub-sample that consisted of 500 features (columns in X) and 2/3 of the samples (rows i […]

SCoRS institution(s)
Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, UK; Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, UK; Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt/Main, Germany; Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Germany; Biomedical Institute, Universidade Federal Fluminense, Brazil; University of Tuebingen, Department of Psychiatry and Psychotherapy, Tuebingen, Germany
SCoRS funding source(s)
Supported by a Wellcome Trust Career Development Fellowship under grant no. WT086565/Z/08/Z, the Wellcome Trust (grant no. WT086565/Z/08/Z), Capes (Coordination for the Improvement of Higher Level Personnel), Brazil (grant no. 3883/11-6), the Kings College Annual Fund and the Kings College London Centre of Excellence in Medical Engineering, funded by the Wellcome Trust and EPSRC under grant no. WT088641/Z/09/Z.

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