Authors@R: c(person("Dario","Azzimonti",role=c("aut","cre","cph"),email= "dario.azzimonti@gmail.com",comment = c(ORCID = "0000-0001-5080-3061")),person("Julien","Bect", role=c("ctb"),email = "julien.bect@centralesupelec.fr"),person("Pedro","Felzenszwalb",role= c("ctb","cph"),comment="Original dt code, http://cs.brown.edu/people/pfelzens/dt/index.html"))

Packaged: 2022-09-08 13:20:11 UTC; dario.azzimonti

Title: Prediction-Based Kinase-Substrate Enrichment Analysis
Authors@R: person("Peter", "Liao", email = "pll21@case.edu", role = c("aut", "cre"))
Description: A tool for inferring kinase activity changes from
phosphoproteomics data. 'pKSEA' uses kinase-substrate
prediction scores to weight observed changes in
phosphopeptide abundance to calculate a phosphopeptide-level
contribution score, then sums up these contribution scores by
kinase to obtain a phosphoproteome-level kinase activity
change score (KAC score). 'pKSEA' then assesses the
significance of changes in predicted substrate abundances for
each kinase using permutation testing. This results in a
permutation score (pKSEA significance score) reflecting the
likelihood of a similarly high or low KAC from random chance,
which can then be interpreted in an analogous manner to an
empirically calculated p-value. 'pKSEA' contains default
databases of kinase-substrate predictions from 'NetworKIN'
and of known kinase-substrate links from 'PhosphoSitePlus'

Authors@R: c(person("Roberto", "Gatta", role = c("aut", "cre"),
person("Jacopo", "Lenkowicz", role = "aut"),
person("Mauro", "Vallati", role = "aut"),
person("Alessandro", "Stefanini", role = "aut"),
person("Nicola", "Dinapoli", role = "ctb"),
person("Berardino", "De Bari", role = "ctb"),
person("Lucia", "Sacchi", role = "ctb"))
Description: Allows to build and train simple Process Mining (PM) models (at the moment only AlphaAlgorithm and First Order Markov Model). The aim is to support PM specifically for the clinical domain from both administrative and clinical data.

Description: Allows to build and train simple Process Mining (PM) models. The aim is to support PM specifically for the clinical domain from both administrative and clinical data.