iggy
:arrow_upper_right: Influence graph analysis, consistency check, diagnosis, repair and prediction
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
:arrow_upper_right: Influence graph analysis, consistency check, diagnosis, repair and prediction
Basic Info
- Host: GitHub
- Owner: bioasp
- License: gpl-3.0
- Language: Rust
- Default Branch: master
- Homepage: https://bioasp.github.io/iggy
- Size: 2.61 MB
Statistics
- Stars: 7
- Watchers: 2
- Forks: 3
- Open Issues: 6
- Releases: 11
Topics
Metadata Files
README.md
iggy + optgraph 
iggy and optgraph are tools for consistency based analysis of influence graphs and observed systems behavior (signed changes between two measured states). For many (biological) systems are knowledge bases available that describe the interaction of its components in terms of causal networks, boolean networks and influence graphs where edges indicate either positive or negative effect of one node upon another.
iggy implements methods to check the consistency of large-scale data sets and provides explanations for inconsistencies. In practice, this is used to identify unreliable data or to indicate missing reactions. Further, iggy addresses the problem of repairing networks and corresponding yet often discrepant measurements in order to re-establish their mutual consistency and predict unobserved variations even under inconsistency.
optgraph confronts interaction graph models with observed systems behavior from multiple experiments. opt_graph computes networks fitting the observation data by removing (or adding) a minimal number of edges in the given network.
Downloads
Precompiled binaries for 64bit linux and macOS can be found on the release page
Sample data demo_data.tar.gz
Compile yourself
Clone the git repository:
sh
git clone https://github.com/bioasp/iggy.git
cargo build --release
The executables can be found under ./target/release/
Iggy
Typical usage is:
sh
iggy -n network.cif -o observation.obs -l 10 -p
For more options you can ask for help as follows:
```txt
iggy -h iggy 2.2.1-dev Sven Thiele sthiele78@gmail.com Iggy confronts interaction graph models with observations of (signed) changes between two measured states (including uncertain observations). Iggy discovers inconsistencies in networks or data, applies minimal repairs, and predicts the behavior for the unmeasured species. It distinguishes strong predictions (e.g. increase in a node) and weak predictions (e.g., the value of a node increases or remains unchanged)
USAGE:
iggy [OPTIONS] --network
OPTIONS:
-a, --auto-inputs Declare nodes with indegree 0 as inputs
--depmat Combine multiple states, a change must be explained by an
elementary path from an input
--elempath Every change must be explained by an elementary path from an
input
--founded-constraints-off Disable foundedness constraints
--fwd-propagation-off Disable forward propagation constraints
-h, --help Print help information
--json Print JSON output
-l, --show-labelings
```
Optgraph
Typical usage is:
sh
optgraph -n network.cif -o observations_dir/ --show-repairs 10
For more options you can ask for help as follows:
```txt
optgraph -h optgraph 2.2.1-dev Sven Thiele sthiele78@gmail.com Optgraph confronts interaction graph models with observations of (signed) changes between two measured states. Opt-graph computes networks fitting the observation data by removing (or adding) a minimal number of edges in the given network
USAGE:
optgraph [OPTIONS] --network
OPTIONS:
-a, --auto-inputs Declare nodes with indegree 0 as inputs
--depmat Combine multiple states, a change must be explained by an
elementary path from an input
--elempath Every change must be explained by an elementary path from an
input
--founded-constraints-off Disable foundedness constraints
--fwd-propagation-off Disable forward propagation constraints
-h, --help Print help information
--json Print JSON output
-m, --repair-mode
Related publications
Designing optimal experiments to discriminate interaction graph models, IEEE/ACM Trans. Comput. Biol. Bioinform, 16(3), 2019.
Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies, BMC Bioinformatics, 2015.
Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming, 12th International Conference on the Principles of Knowledge Representation and Reasoning, 2010.
Directed random walks and constraint programming reveal active pathways in HGF signaling, FEBS Journal, 2015.
Owner
- Name: BioASP
- Login: bioasp
- Kind: organization
- Email: bioasp@gmail.com
- Website: bioasp.github.io
- Repositories: 16
- Profile: https://github.com/bioasp
ASP-based software for Systems Biology
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sven Thiele | s****8@g****m | 335 |
| dependabot[bot] | 4****] | 14 |
| Sven Thiele | s****e@u****e | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 100
- Average time to close issues: N/A
- Average time to close pull requests: 17 days
- Total issue authors: 0
- Total pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.91
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 96
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- dependabot[bot] (96)
- sthiele (4)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 54 last-month
- Total dependent packages: 0
- Total dependent repositories: 2
- Total versions: 10
- Total maintainers: 1
pypi.org: iggy
A tool for consistency based analysis of influence graphs and observed systems behavior.
- Homepage: http://bioasp.github.io/iggy/
- Documentation: https://iggy.readthedocs.io/
- License: GPLv3+
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Latest release: 1.4.3
published over 7 years ago
Rankings
Maintainers (1)
Dependencies
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- actions/checkout v2 composite
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