# MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction

arXiv: https://arxiv.org/abs/2108.04820 · HTML: https://mlsanity.com/paper/2108.04820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems](https://mlsanity.com/paper/2001.02223/) (2020)

## Built on by (most cited first, 4 of 4)

- [Private Graph Extraction via Feature Explanations](https://mlsanity.com/paper/2206.14724/) (2022)
- [BAGEL: A Benchmark for Assessing Graph Neural Network Explanations](https://mlsanity.com/paper/2206.13983/) (2022)
- [Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?](https://mlsanity.com/paper/2306.00578/) (2023)
- [Privacy and Transparency in Graph Machine Learning: A Unified Perspective](https://mlsanity.com/paper/2207.10896/) (2022)

## Abstract (arXiv)

Growing evidence from recent studies implies that microRNA or miRNA could serve as biomarkers in various complex human diseases. Since wet-lab experiments are expensive and time-consuming, computational techniques for miRNA-disease association prediction have attracted a lot of attention in recent years. Data scarcity is one of the major challenges in building reliable machine learning models. Data scarcity combined with the use of precalculated hand-crafted input features has led to problems of overfitting and data leakage. We overcome the limitations of existing works by proposing a novel multi-tasking graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (interactions between miRNA/diseases and protein-coding genes (PCG), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multi-task setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we construct large-scale experiments on standard benchmark datasets as well as our proposed larger independent test sets and case studies. MuCoMiD shows an improvement of at least 3% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 35% on larger independent test sets with unseen miRNA and diseases over state-of-the-art approaches. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.