# Relation-weighted Link Prediction for Disease Gene Identification

arXiv: https://arxiv.org/abs/2011.05138 · HTML: https://mlsanity.com/paper/2011.05138/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach](https://mlsanity.com/paper/2305.10257/) (2023)
- [Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction](https://mlsanity.com/paper/2405.02628/) (2024)

## Abstract (arXiv)

Identification of disease genes, which are a set of genes associated with a disease, plays an important role in understanding and curing diseases. In this paper, we present a biomedical knowledge graph designed specifically for this problem, propose a novel machine learning method that identifies disease genes on such graphs by leveraging recent advances in network biology and graph representation learning, study the effects of various relation types on prediction performance, and empirically demonstrate that our algorithms outperform its closest state-of-the-art competitor in disease gene identification by 24.1%. We also show that we achieve higher precision than Open Targets, the leading initiative for target identification, with respect to predicting drug targets in clinical trials for Parkinson's disease.