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cff-version: 1.2.0
message: 'To cite package "kgraph" in publications use:'
type: software
license: GPL-3.0-only
title: 'kgraph: Knowledge Graphs Constructions and Visualizations'
version: 1.0.0
doi: 10.32614/CRAN.package.kgraph
abstract: 'Knowledge graphs enable to efficiently visualize and gain insights into
  large-scale data analysis results, as p-values from multiple studies or embedding
  data matrices. The usual workflow is a user providing a data frame of association
  studies results and specifying target nodes, e.g. phenotypes, to visualize. The
  knowledge graph then shows all the features which are significantly associated with
  the phenotype, with the edges being proportional to the association scores. As the
  user adds several target nodes and grouping information about the nodes such as
  biological pathways, the construction of such graphs soon becomes complex. The ''kgraph''
  package aims to enable users to easily build such knowledge graphs, and provides
  two main features: first, to enable building a knowledge graph based on a data frame
  of concepts relationships, be it p-values or cosine similarities; second, to enable
  determining an appropriate cut-off on cosine similarities from a complete embedding
  matrix, to enable the building of a knowledge graph directly from an embedding matrix.
  The ''kgraph'' package provides several display, layout and cut-off options, and
  has already proven useful to researchers to enable them to visualize large sets
  of p-value associations with various phenotypes, and to quickly be able to visualize
  embedding results. Two example datasets are provided to demonstrate these behaviors,
  and several live ''shiny'' applications are hosted by the CELEHS laboratory and
  Parse Health, as the KESER Mental Health application <https://keser-mental-health.parse-health.org/>
  based on Hong C. (2021) <https://doi.org/10.1038/s41746-021-00519-z>.'
authors:
- family-names: Charlon
  given-names: Thomas
  email: charlon@protonmail.com
  orcid: https://orcid.org/0000-0001-7497-0470
- name: CELEHS
  website: https://celehs.hms.harvard.edu
- name: PARSE Health
  website: https://parse-health.org
repository: https://thomaschln.r-universe.dev
repository-code: https://gitlab.com/thomaschln/kgraph
commit: 979c8d1cccd708d897a689394c1db14e258a4d49
url: https://gitlab.com/thomaschln/kgraph
contact:
- family-names: Charlon
  given-names: Thomas
  email: charlon@protonmail.com
  orcid: https://orcid.org/0000-0001-7497-0470