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

• URL: http://arxiv.org/abs/2108.04820v1
• Date: Sun, 8 Aug 2021 10:01:46 GMT
• Title: MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction
• Authors: Thi Ngan Dong and Megha Khosla
• Abstract summary: We propose a novel multi-tasking convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources. 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 5% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 49% on larger independent test sets with unseen diseases and unseen diseases over state-of-the-art approaches.
• Score: 0.4061135251278187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 pre-calculated 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 convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources (interactions between miRNA/diseases and protein-coding genes (PCG), miRNA family information, and disease ontology) in a multi-task setting which is a novel perspective and has not been studied before. The use of multi-channel convolutions allows us to extract expressive representations while keeping the model linear and, therefore, simple. 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 5% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 49% 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.

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