Readthedocs initial commit

parent 0f6fb60a
...@@ -10,23 +10,11 @@ build: ...@@ -10,23 +10,11 @@ build:
os: ubuntu-22.04 os: ubuntu-22.04
tools: tools:
python: "3.9" python: "3.9"
# You can also specify other tool versions:
# nodejs: "19"
# rust: "1.64"
# golang: "1.19"
# Build documentation in the "docs/" directory with Sphinx # Build documentation in the "docs/" directory with Sphinx
sphinx: sphinx:
configuration: docs/conf.py configuration: docs/source/conf.py
# Optionally build your docs in additional formats such as PDF and ePub python:
# formats: install:
# - pdf - requirements: docs/requirements.txt
# - epub \ No newline at end of file
# Optional but recommended, declare the Python requirements required
# to build your documentation
# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
# python:
# install:
# - requirements: docs/requirements.txt
\ No newline at end of file
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=source
set BUILDDIR=build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
sphinx==7.1.2
sphinx-rtd-theme==1.3.0rc1
numpy
pandas
wget
scipy
networkx
torch>=1.12
matplotlib
seaborn
tqdm
scikit-learn
pytorch-lightning==1.9.0
torchmetrics
biopython
fair-esm
\ No newline at end of file
API
===
.. autosummary::
:toctree: generated
# Configuration file for the Sphinx documentation builder.
# -- Project information
project = 'SENSE-PPI'
copyright = '2023, Konstantin Volzhenin, Lucie Bittner, Alessandra Carbone'
author = 'Konstantin Volzhenin, Lucie Bittner, Alessandra Carbone'
release = '0.1'
version = '0.1.0'
# -- General configuration
extensions = [
'sphinx.ext.duration',
'sphinx.ext.doctest',
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
]
intersphinx_mapping = {
'python': ('https://docs.python.org/3/', None),
'sphinx': ('https://www.sphinx-doc.org/en/master/', None),
}
intersphinx_disabled_domains = ['std']
templates_path = ['_templates']
# -- Options for HTML output
html_theme = 'sphinx_rtd_theme'
# -- Options for EPUB output
epub_show_urls = 'footnote'
Welcome to SENSE-PPI documentation!
===================================
**SENSE-PPI** is a Deep Learning model for predicting physical protein-protein interactions based on amino acid sequences.
It is based on embeddings generated by ESM2 and uses Siamese RNN architecture to perform a binary classification.
Check out the :doc:`usage` section for further information, including
how to :ref:`installation` the project.
.. note::
This project is under active development.
Contents
--------
.. toctree::
usage
api
Usage
=====
.. _installation:
Installation
------------
To use SENSE-PPI, first install it using pip:
.. code-block:: console
(.venv) $ pip install senseppi
Commands
------------
There are 5 commands available in the package:
- `train`: trains SENSE-PPI on a given dataset
- `test`: computes test metrics (AUROC, AUPRC, F1, MCC, Presicion, Recall, Accuracy) on a given dataset
- `predict`: predicts interactions for a given dataset
- `predict_string`: predicts interactions for a given dataset using STRING database: the interactions are taken from the STRING database (based on seed proteins). Predictions are compared with the STRING database. Optionally, the graphs can be constructed.
- `create_dataset`: creates a dataset from the STRING database based on the taxonomic ID of the organism.
[build-system]
requires = ["flit_core >=3.2,<4"]
build-backend = "flit_core.buildapi"
[project]
name = "senseppi"
authors = [{name = "Konstantin Volzhenin", email = "konstantin_v_v@outlook.com"}]
dynamic = ["version", "description"]
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