  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.

Quantitative Methods;Machine Learning