DL2Vec

This example corresponds to the paper Predicting candidate genes from phenotypes, functions and anatomical site of expression.

This work is a graph-based machine-learning method to learn from biomedical ontologies. This method works by transforming the ontology into a graph following a set of rules. Random walks are generated from the obtained graph and then processed by a Word2Vec model, which generates embeddings of the original ontology classes. This algorithm is applied to generate numerical representations of genes and diseases based on the background knowledge found in the Gene Ontology, which was extended to incorporate phenotypes, functions of the gene products and anatomical location of gene expression. The representations of genes and diseases are then used to predict candidate genes for a given disease.

To show an example of DL2Vec, we need 3 components:

• The ontology projector

• The random walks generator

• The Word2Vec model

import sys
sys.path.append('../../')
import mowl
mowl.init_jvm("10g")

from mowl.datasets.builtin import GDADatasetV2
from mowl.models import RandomWalkPlusW2VModel
from mowl.projection import DL2VecProjector
from mowl.walking import DeepWalk
from gensim.models.word2vec import LineSentence
from gensim.models import Word2Vec

Instantiating the dataset and the model

dataset = GDADatasetV2()

print(f"Number of classes: f{len(dataset.classes)}")

model = RandomWalkPlusW2VModel(dataset)
model.set_projector(DL2VecProjector())
model.set_walker(DeepWalk(5, 5, 0.1, workers=4))
model.set_w2v_model(vector_size=5, epochs=2, window=5, min_count=1, workers=4)
model.train()

Number of classes: f197774

Evaluating the model

from mowl.evaluation import GDAEvaluator
model.set_evaluator(GDAEvaluator)
model.evaluate(dataset.testing)

Estimated memory usage: 5145 MB