SCoRS specifications


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


No version available

Publication for Survival Count on Random Subsamples

SCoRS citation


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

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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