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CORNETO (Constraint-based Optimization for the Reconstruction of NETworks from Omics) is a unified network inference framework implemented in Python designed to bring together many common network inference problems in biology. Through constraint programming, CORNETO transforms these problems into unified mathematical representations using flow networks, offering modular building blocks for diverse applications. It accommodates a wide range of network inference problems, from basic analyses like shortest paths or Steiner trees for Protein-Protein Interactions (PPIs), to more advance problems such as contextualising signalling networks from directed signed prior knowledge networks or inferring metabolic networks from Genome-Scale Metabolic Network models, effectively harnessing the capabilities of Flux Balance Analysis.

> **NOTE**: This is an early preview of the library, which includes a very limited subset of methods for signalling, and an early version of the API to build optimization problems. We're currently working towards having a final version including additional and novel methods.
> **NOTE**: This is an early preview of the library, which includes a very limited subset of methods for signalling, and an early version of the API to build optimization problems. We're currently working towards having a final version including additional and novel methods, as well as full support for Flux Balance Analysis (FBA)
## Installation

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CORNETO does not include any backend nor solver by default to avoid issues with architectures for which some of the required binaries are not available. The recommended setup for CORNETO requires CVXPY and Gurobi:

```bash
pip install corneto cvxpy gurobipy
pip install corneto cvxpy scipy gurobipy
```

Please note that **GUROBI is a commercial solver which offers free academic licenses**. If you have an academic email, this step is very easy to do in just few minutes: https://www.gurobi.com/features/academic-named-user-license/.
Alternatively, it is possible to use CORNETO with any free solver, such as HIGHS, included in Scipy. To install CORNETO with support for HIGHs, you only need to install the latest version of scipy:
Please note that **GUROBI is a commercial solver which offers free academic licenses**. If you have an academic email, this step is very easy to do in just few minutes: https://www.gurobi.com/features/academic-named-user-license/. You need to register GUROBI in your machine with the `grbgetkey` tool from GUROBI.

```bash
pip install corneto cvxpy scipy
Alternatively, it is possible to use CORNETO with any free solver, such as HIGHS, included in Scipy. For this you don't need to install Gurobi. Please note that if `gurobipy` is installed but not registered with a valid license, CORNETO will choose it but the solver will fail due to license issues. If SCIPY is installed, when optimizing a problem, select SCIPY as the solver

```python
# P is a corneto problem
P.solve(solver="SCIPY")
```

> :warning: Please note that without any backend, you can't do much with CORNETO. There are two supported backends right now: [PICOS](https://picos-api.gitlab.io/picos/tutorial.html) and [CVXPY](https://www.cvxpy.org/). Both backends allow symbolic manipulation of expressions in matrix notation.
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CORNETO is developed at the [Institute for Computational Biomedicine](https://saezlab.org) (Heidelberg University). The development of this project is supported by European Union's Horizon 2020 Programme under
PerMedCoE project ([permedcoe.eu](https://permedcoe.eu/)) agreement no. 951773.

<img src="https://raw.githubusercontent.com/saezlab/.github/main/profile/logos/saezlab.png" alt="Saez lab logo" height="64px" style="height:64px; width:auto"> <img src="https://lcsb-biocore.github.io/COBREXA.jl/stable/assets/permedcoe.svg" alt="PerMedCoE logo" height="64px" style="height:64px; width:auto"> <img src="https://www.klinikum.uni-heidelberg.de/typo3conf/ext/site_ukhd/Resources/Public/Images/Logo_ukhd_de.svg" alt="UKHD logo" height="64px" style="height:64px; width:auto">
<img src="https://raw.githubusercontent.com/saezlab/.github/main/profile/logos/saezlab.png" alt="Saez lab logo" height="64px" style="height:64px; width:auto"> <img src="https://lcsb-biocore.github.io/COBREXA.jl/stable/assets/permedcoe.svg" alt="PerMedCoE logo" height="64px" style="height:64px; width:auto"> <img src="https://yt3.googleusercontent.com/ytc/AIf8zZSHTQJs12aUZjHsVBpfFiRyrK6rbPwb-7VIxZQk=s176-c-k-c0x00ffffff-no-rj" alt="UKHD logo" height="64px" style="height:64px; width:auto">

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