Title: Estimation, Simulation and Reliability of Drifting Markov Models
Authors@R: c(person("Vlad Stefan", "Barbu", role = "aut",
person("Geoffray", "Brelurut", role = "ctb"),
person("Annthomy", "Gilles", role = "ctb"),
person("Arnaud", "Lefebvre", role = "ctb"),
person("Corentin", "Lothode", role = "aut",
person("Victor", "Mataigne", role = "ctb"),
person("Alexandre", "Seiller", role = "aut"),
person("Nicolas", "Vergne", role = c("aut","cre"),
Description: Performs the drifting Markov models (DMM) which are
non-homogeneous Markov models designed for modeling the heterogeneities of
sequences in a more flexible way than homogeneous Markov chains or even
hidden Markov models. In this context, we developed an R package dedicated to
the estimation, simulation and the exact computation of associated reliability
of drifting Markov models. The implemented methods are described in
Imports: seqinr, Biostrings, testthat, stats, ggplot2, parallel,
future, doParallel, foreach, tidyverse, dplyr, reshape2, Rdpack

Imports: seqinr, ggplot2, parallel, future, doParallel, foreach,

Title: Prioritizing Cancer Driver Genes Using Genomics Data
Description: Cancer genomes contain large numbers of somatic alterations but few
genes drive tumor development. Identifying molecular cancer driver genes is
critical for precision oncology. Most of current approaches either identify
driver genes based on mutational recurrence or using estimated scores
predicting the functional consequences of mutations. 'driveR' is a tool for
personalized or batch analysis of genomic data for driver gene prioritization
by combining genomic information and prior biological knowledge. As features,
'driveR' uses coding impact metaprediction scores, non-coding impact scores,
somatic copy number alteration scores, hotspot gene/double-hit gene
condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG
pathways. It uses these features to estimate cancer-type-specific
probability for each gene of being a cancer driver using the related task of
a multi-task learning classification model. The method is described in detail
in Ulgen E, Sezerman U. 2020. driveR: A Novel Method for Prioritizing Cancer
Driver Genes Using Somatic Genomics Data. bioRxiv
Imports: caret, randomForest, Homo.sapiens, GenomicRanges,
Author: Ege Ulgen [aut, cre, cph] (<https://orcid.org/0000-0003-2090-3621>)