# anc2vec
**Repository Path**: northpoleforce/anc2vec
## Basic Information
- **Project Name**: anc2vec
- **Description**: folk
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-01-20
- **Last Updated**: 2024-01-20
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Anc2vec
Anc2vec is a novel method based on neural networks to construct embeddings of terms from the [Gene Ontology](http://geneontology.org/) (GO) exclusively using three structural features of it: the ontological uniqueness of terms, their ancestor relationships and the sub-ontology to which they belong.
This repository offers a Python package containing the source code of anc2vec,
as well as instructions for reproducibility of the main results of the study
where this method was proposed:
*[Anc2vec: embedding Gene Ontology terms by preserving
ancestors relationships](ms.pdf)*, by A. A. Edera, D. H. Milone, and G. Stegmayer. Research Institute for Signals, Systems and Computational
Intelligence, [sinc(i)](https://sinc.unl.edu.ar).
Fig. 1. Panel A) The GO structure is composed by hierarchical
relationships between terms arranged in three sub-ontologies: BP, CC, and
MF. Panel B) Anc2vec architecture. A GO term is encoded as a one-hot vector
x that is transformed into an embedding h, which is used to predict three
structural features of the GO that are used for weight optimization.
Fig. 2. Anc2vec embeddings of GO terms in the three
sub-ontologies. Points depict embeddings of GO terms whose colors encode the
sub-ontologies: BP (Biological Process), CC (Cellular Component), and MF
(Molecular Function). There is available a video showing how
2-dimensional embeddings are adjusted during weight optimization.
## Requirements
Anc2vec requires [Python](https://www.python.org/) 3.6 and
[TensorFlow](https://www.tensorflow.org/) 2.3.1.
## Installation
It is recommendable to have installed
[Conda](https://docs.conda.io/en/latest/), to avoid Python package conflicts.
If Conda is installed, first create and activate a conda environment, for
example, named anc2vec:
```bash
conda create --name anc2vec python=3.6
conda activate anc2vec
```
Next, install the `anc2vec` package via the
[pip package manager](https://pip.pypa.io/en/stable/installing/):
```bash
pip install -U "anc2vec @ git+https://github.com/aedera/anc2vec.git"
```
## Anc2vec functionalities
### Access pre-trained embeddings
The `anc2vec` package has already available the same embedding of GO terms
used in the study. These embeddings were built using the Gene Ontology release
[2020-10-06](./anc2vec/data/go.obo). The embeddings can be easily accessed on
Python with this command:
```python
import anc2vec
es = anc2vec.get_embeddings()
```
Here, `es` is a python dictionary that maps GO terms with their corresponding
200-dimensional embeddings. For example, this command uses this dictionary to
retrieve the embedding corresponding to the term `GO:0001780`:
```python
e = es['GO:0001780']
```
The variable `e` is a [Numpy](https://numpy.org/) array containing the
embedding
```python
array([ 0.55203265, -0.23133564, 0.1983797 , -0.3251996 , 0.20564775,
-0.32133245, -0.25364587, -0.16675541, -0.46832997, -0.40702957,
...
-0.29757708, -0.33143485, -0.31099185, 0.24465033, -0.25458524,
-0.24525951, -0.366758 , -0.04628978, 0.29378492, 0.31249675],
dtype=float32)
```
These `anc2vec` embeddings are ready to be used for semantic similarity
tasks. Below there are examples showing how to use them for calculating
[cosine distances](https://en.wikipedia.org/wiki/Cosine_similarity).
### Build your own embeddings
The `anc2vec` package also contains a function to build embeddings from
scratch using a specific
[OBO file](http://owlcollab.github.io/oboformat/doc/obo-syntax.html), a
human-readable file usually used to describe the GO. Building embeddings can
be particularly useful for experimental scenarios where a specific version of
the GO is required, such as those available in the
[GO data archive](http://release.geneontology.org/).
The following code shows how to build the embedding for a given OBO file named
`go.obo`.
```python
import anc2vec
import anc2vec.train as builder
es = builder.fit('go.obo', embedding_sz=200, batch_sz=64, num_epochs=100)
```
The object `builder` uses the input `go.obo` file to extract structural
features used to build the embeddings of GO terms. Note that `builder` is
called with additional parameters indicating the dimensionality of the
embeddings (`embedding_sz`) and the number of optimization steps used for
embedding building (`num_epochs`). The embeddings built by `builder` are
stored in `es`, which is a Python dictionary mapping GO terms to their
corresponding embeddings.
Please check the examples below for more information about this functionality.
## Notebooks: examples on how to use the `anc2vec` package
To try anc2vec, below there are links to
[Jupyter notebooks](https://jupyter.org) that use
[Google Colab](https://research.google.com/colaboratory/) which offers free
computing on the Google cloud.
* [Using `anc2vec` pre-trained embeddings](https://colab.research.google.com/github/aedera/anc2vec/blob/main/examples/pretrained_anc2vec_embeddings.ipynb)
* [Projecting `anc2vec` pre-trained embeddings](https://colab.research.google.com/github/aedera/anc2vec/blob/main/examples/project_embeddings.ipynb)
* [Building `anc2vec` embeddings for a desired obo file](https://colab.research.google.com/github/aedera/anc2vec/blob/main/examples/train_anc2vec_embeddings.ipynb)
## Datasets
These are the main datasets used in the experiments of the study where anc2vec
is proposed:
* [Ancestors dataset](https://drive.google.com/file/d/1fgK50TNg5nrade22SwmqZYOeAxgPHIHY/view?usp=sharing)
* [Protein function dataset](https://drive.google.com/file/d/1eokaKj20tbFTn9jexQXIkONqwHeiBGS-/view?usp=sharing)
* [STRING dataset](https://drive.google.com/file/d/1dBZqQeBuGf35_pGT6qJWSuX1At32t9CI/view?usp=sharing)
## License
The `anc2vec` package is released under the [MIT License](LICENSE).
