  Background: The learning of genotype-phenotype associations and history of
human disease by doing detailed and precise analysis of phenotypic
abnormalities can be defined as deep phenotyping. To understand and detect this
interaction between phenotype and genotype is a fundamental step when
translating precision medicine to clinical practice. The recent advances in the
field of machine learning is efficient to predict these interactions between
abnormal human phenotypes and genes.
  Methods: In this study, we developed a framework to predict links between
human phenotype ontology (HPO) and genes. The annotation data from the
heterogeneous knowledge resources i.e., orphanet, is used to parse human
phenotype-gene associations. To generate the embeddings for the nodes (HPO &
genes), an algorithm called node2vec was used. It performs node sampling on
this graph based on random walks, then learns features over these sampled nodes
to generate embeddings. These embeddings were used to perform the downstream
task to predict the presence of the link between these nodes using 5 different
supervised machine learning algorithms.
  Results: The downstream link prediction task shows that the Gradient Boosting
Decision Tree based model (LightGBM) achieved an optimal AUROC 0.904 and AUCPR
0.784. In addition, LightGBM achieved an optimal weighted F1 score of 0.87.
Compared to the other 4 methods LightGBM is able to find more accurate
interaction/link between human phenotype & gene pairs.

Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing