diffuStats: an R package to compute diffusion-based scores on biological networks
Author(s) -
Sergio PicartArmada,
Wesley K. Thompson,
Alfonso Buil,
Alexandre Perera-Lluna
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx632
Subject(s) - bioconductor , computer science , r package , normalization (sociology) , benchmarking , computation , computational statistics , permutation (music) , graph , transferability , data mining , kernel (algebra) , theoretical computer science , machine learning , algorithm , mathematics , computational science , biology , physics , biochemistry , logit , marketing , combinatorics , sociology , gene , anthropology , acoustics , business
Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritizing novel candidates in the area of study. There are several choices in conceiving the diffusion process-involving the graph kernel, the score definitions and the presence of a posterior statistical normalization-which have an impact on the results. This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalized scores, that eases the computation of the scores and their benchmarking for an optimal choice.
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