An Algorithm for Automatic Peak Detection in Noisy Periodic and
Quasi-Periodic Signals
A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima.
For further information see  <doi:10.3390/a5040588>.

c(person("Legana","Fingerhut",
email = "legana.fingerhut@my.jcu.edu.au",
role = c("aut", "cre")),
person("Ira","Cooke",
email = "ira.cooke@jcu.edu.au",
role = c("aut")),
person("Jinlong","Zhang",
email = "jinlongzhang01@gmail.com",
role = c("ctb"),
comment = c("R/read_faa.R")),
person("Nan","Xiao",
email = "me@nanx.me",
role = c("ctb"),
comment = c("R/calc_pseudo_comp.R")))
A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
It incorporates a support vector machine model trained on publicly available antimicrobial peptide data using calculated
physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
In order to support genome-wide analyses this model is designed to accept any type of protein as input
and calculation of compositional properties has been optimised for high-throughput use.
Peptides, caret (>= 6.0.0), kernlab, Rcpp
Legana Fingerhut [aut, cre],
Ira Cooke [aut],
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R)

c(person("Legana","Fingerhut",
email = "legana.fingerhut@my.jcu.edu.au",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-2482-5336")),
person("Ira","Cooke",
email = "ira.cooke@jcu.edu.au",
role = c("aut"),
comment = c(ORCID = "0000-0001-6520-1397")),
person("Jinlong","Zhang",
email = "jinlongzhang01@gmail.com",
role = c("ctb"),
comment = c("R/read_faa.R")),
person("Nan","Xiao",
email = "me@nanx.me",
role = c("ctb"),
comment = c("R/calc_pseudo_comp.R")))
A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated
physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
In order to support genome-wide analyses, these models are designed to accept any type of protein as input
and calculation of compositional properties has been optimised for high-throughput use. For details see Fingerhut et al. 2020 <doi:10.1101/2020.05.07.082412>.
Peptides, caret (>= 6.0.0), kernlab, Rcpp, parallel
Legana Fingerhut [aut, cre] (<https://orcid.org/0000-0002-2482-5336>),
Ira Cooke [aut] (<https://orcid.org/0000-0001-6520-1397>),
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R)