Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (6.0%) to scientific vocabulary
Repository
Algorithm for knowledge graph embedding (KGE) generation
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
DLemb
DLemb is an algorithm that creates node embeddings from a knowledge graph. The knowledge graph has to be fed to the algorithm in a .csv format without index and with specific headers i.e.
- source: id of the source
- target: id of the target
- rel_type: type of the interaction
An example is available in data/kg_edgelist.csv. This knowledge graph is composed of 41k nodes of 4 types (functions, phenotypes, drugs and proteins) and ~60 types of relationships.
DLemb need several dependencies, an environment named DLemb can be created by running
conda env create -f DLemb.yml
Then activate the environment with
conda activate DLemb
The algorithm consists of a shallow neural network implemented in Keras, it is composed by an Embedding layer and a normalized Dot layer. It works by randomly generating negative (wrong) triplets that are used to train the network: once the Dot product is computed the error is calculated trough RMSE and the weights of the embedding layer are optimized trouhg Adam optimization.
The customizable flags of DLemb can be listed by running:
./DLemb.py -h
DLemb automatically runs on GPU is available. A typical call for DLemb is as follows:
./DLemb.py -i data/kg_edgelist.csv -o outputs/kg_embeddings.pickle
Owner
- Name: Francesco Gualdi
- Login: freh-g
- Kind: user
- Location: Barcelona
- Company: Institut Hospital del Mar d'investigacions mediques (IMIM)
- Repositories: 0
- Profile: https://github.com/freh-g
Bsc in biology at UNIFE, Msc in medical biotechnology at UNIVR. Currently working in medical bioinformatics group at IMIM, Barcelona doing my PhD in Biomedicine