Description: Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors.

Description: Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Title: Anthology of Mixture Analysis Tools
Authors@R: c(person("Raffaele", "Argiento", role = "aut", email= "raffaele.argiento@unito.it"),
person("Bruno", "Bodin", role = c("aut", "cre"), email = "bruno.bodin@yale-nus.edu.sg"),
person("Maria", "De Iorio", role = "aut", email = "m.deiorio@ucl.ac.uk"))
Description: Fits finite Bayesian mixture models with random number of component. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computational efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, univariate Poisson, univariate binomial, multivariate Gaussian, multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).
Imports: stats, graphics, grDevices, Rcpp (>= 0.12.3), sdols, mvtnorm,
Suggests: dendextend, ggdendro, ggplot2, jpeg

Authors@R: c(person("Priscilla", "Ong", role = c("aut", "edt"), email = "priscilla.ong@u.yale-nus.edu.sg"),
person("Raffaele", "Argiento", role = "aut", email= "raffaele.argiento@unito.it"),
Description: Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).
Imports: stats, graphics, grDevices, Rcpp (>= 0.12.3), salso, mvtnorm,