Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in recent years. A big challenge in building reliable machine learning models is that of data scarcity. In particular, existing approaches trained on the available small datasets, even when combined with precalculated handcrafted input features, often suffer from bad generalization and data leakage problems. We overcome the limitations of existing works by proposing a novel multitask graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (associations between miRNAs/diseases and protein-coding genes (PCGs), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multitask setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we conduct large-scale experiments on the standard benchmark datasets as well as on our proposed large independent testing sets and case studies. MuCoMiD obtains significantly higher Average Precision (AP) scores than all benchmarked models on three large independent testing sets, especially those with many new miRNAs, as well as in the detection of false positives. Thanks to its capability of learning directly from raw input information, MuCoMiD is easier to maintain and update than handcrafted feature-based methods, which would require recomputation of features every time there is a change in the original information sources (e.g., disease ontology, miRNA/disease-PCG associations, etc.). We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.

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Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.@en