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:arrow_upper_right: Influence graph analysis, consistency check, diagnosis, repair and prediction

iggy 최신버젼 다운로드

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# `iggy` + `optgraph` [![Build Status](https://github.com/bioasp/iggy/workflows/CI%20Test/badge.svg)](https://github.com/bioasp/iggy)

`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](https://github.com/bioasp/iggy/releases/latest)

- [Iggy user guide](https://bioasp.github.io/iggy/guide/guide.html)

- Sample data [demo_data.tar.gz](https://bioasp.github.io/iggy/downloads/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 <FILE>

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 <N>         Show N labelings, default is OFF, 0=all
        --mics                       Compute minimal inconsistent cores
    -n, --network <FILE>             Influence graph in CIF format
    -o, --observations <FILE>        Observations in bioquali format
    -p, --show-predictions           Show predictions
        --scenfit                    Compute scenfit of the data, default is mcos
    -V, --version                    Print version information

```

## 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 <FILE> --observations <DIR>

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 <REPAIR_MODE>    REPAIR_MODE: remove = remove edges (default), optgraph = add
                                       + remove edges, flip = flip direction of edges
    -n, --network <FILE>               Influence graph in CIF format
    -o, --observations <DIR>           Directory of observations in bioquali format
    -r, --show-repairs <N>             Show N repairs, default is OFF, 0=all
    -V, --version                      Print version information
```

## Related publications

- [*Designing optimal experiments to discriminate interaction graph models*](https://doi.org/10.1109/TCBB.2018.2812184), IEEE/ACM Trans. Comput. Biol. Bioinform, 16(3), 2019.

- [*Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies*](http://dx.doi.org/10.1186/s12859-015-0733-7), BMC Bioinformatics, 2015.

- [*Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming*](http://aaai.org/ocs/index.php/KR/KR2010/paper/view/1334/1660), 12th International Conference on the Principles of Knowledge Representation and Reasoning, 2010.

- [*Directed random walks and constraint programming reveal active pathways in HGF signaling*](http://dx.doi.org/10.1111/febs.13580), FEBS Journal, 2015.

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