An implementation of the maximal
peptide ratio extraction module of the MaxLFQ algorithm by
Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a complete
pipeline for processing proteomics data in data-independent acquisition mode
(Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
It offers additional options for protein quantification using the N most intense
fragment ions, using all
fragment ions, and a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).

An implementation of the maximal
peptide ratio extraction module of the MaxLFQ algorithm by
Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a complete
pipeline for processing proteomics data in data-independent acquisition mode
(Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
It offers additional options for protein quantification using
the N most intense fragment ions, using all fragment ions, and
a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).

c(person("Thang", "Pham", email = "t.pham@amsterdamumc.nl",
role = c("aut", "cre", "cph", "ctb"),
comment = c(ORCID = "0000-0003-0333-2492")),
person("Alex", "Henneman",
role = c("ctb"),
comment = c(ORCID = "0000-0002-3746-4410")))
An implementation of the MaxLFQ algorithm by
Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive
pipeline for processing proteomics data in data-independent acquisition mode
(Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
It offers additional options for protein quantification using
the N most intense fragment ions, using all fragment ions, and
a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).
In general, the tool can be used to integrate multiple
proportional observations into a single quantitative value.

Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski
Estimate an optimal dynamic treatment regime using Interactive Q-learning

Estimate an optimal dynamic treatment regime using Interactive Q-learning.

Generate Chemical Strings (SMILES) with the Inverse QSPR Model
Generate chemical structures possibly satisfying desired properties
using the inverse QSPR model. It has three reference classes. ENgram is a class for learning the grammar
structure of existing chemical strings using an extended N-gram model. QSPRpred contains
a simple Bayes regression model to predict properties from structures. SmcChem is a class of the generator of
chemical strings from the Inverse-QSPR model. This class has ENgram and QSPRpred class objects inside.
The generator is implemented by the Sequential Monte Carlo sampler.
OpenBabel (>= 2.3.1) with headers.
http://openbabel.org