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Papers: 10.1186/1751-0473-8-2

https://doi.org/10.1186/1751-0473-8-2

Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data

Cited by: 7
Author(s): Grzegorz Zycinski, Annalisa Barla, Margherita Squillario, Tiziana Sanavia, Barbara Di Camillo, Alessandro Verri
Published: almost 14 years ago

Software Mentions 8

bioconductor: GOstats
Tools for manipulating GO and microarrays
Papers that mentioned: 368
Very Likely Science (100)
cran: experiment
R Package for Designing and Analyzing Randomized Experiments
Papers that mentioned: 3
Very Likely Science (100)
pypi: experiment
The Experiment package delivers a library of stimulation and recording objects.
Papers that mentioned: 3
Very Likely Science (90)
pypi: fastsemsim
FastSemSim is a Python package to calculate semantic similarity over ontologies (including the Gene Ontology).
Papers that mentioned: 1
Very Likely Science (65)
pypi: KDVS
Knowledge Driven Variable Selection (KDVS) is an experimental knowledge extraction system that utilizes statistical learning and novel data and knowledge integration methodologies.
Papers that mentioned: 3
Very Likely Science (90)
pypi: L1L2Py
L1L2Py is a Python package to perform variable selection by meansof l1l2 regularization with double optimization.
Papers that mentioned: 2
Very Likely Science (90)
pypi: PPlus
A Parallel Python Environment with easy data sharing
Papers that mentioned: 2
Very Likely Science (80)
pypi: scipy
Fundamental algorithms for scientific computing in Python
Papers that mentioned: 1,071
Very Likely Science (100)