# Graph Based Link Prediction between Human Phenotypes and Genes

arXiv: https://arxiv.org/abs/2105.11989 · HTML: https://mlsanity.com/paper/2105.11989/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Attributed Social Network Embedding](https://mlsanity.com/paper/1705.04969/) (2017)

## Built on by (most cited first, 5 of 5)

- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [TREE-G: Decision Trees Contesting Graph Neural Networks](https://mlsanity.com/paper/2207.02760/) (2022)
- [PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2402.01809/) (2024)
- [Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings](https://mlsanity.com/paper/2509.07905/) (2025)

## Abstract (arXiv)

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.