PPInfer
Bioc currentInferring functionally related proteins using protein interaction networks
Release Lineage
Entered 3.5 · Apr 25, 2017
Current · Requires R 4.6
Description
Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
10 0 exported
Complexity
4.8 avg / 12 max
Call network
10 nodes / 1 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
2,133
Files
27
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
10
Internal functions
0
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
–
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
19
First release
2017-10-12
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
5
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Topics
Depended on by (1)
Bioconductor (1)
People
Dongmin Jung
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("PPInfer")Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.