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Article overview
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Self-supervised Learning for Label Sparsity in Computational Drug Repositioning | | Xinxing Yang
; Genke Yang
; Jian Chu
; | | Date: |
1 Jun 2022 | | Abstract: | The computational drug repositioning aims to discover new uses for marketed
drugs, which can accelerate the drug development process and play an important
role in the existing drug discovery system. However, the number of validated
drug-disease associations is scarce compared to the number of drugs and
diseases in the real world. Too few labeled samples will make the
classification model unable to learn effective latent factors of drugs,
resulting in poor generalization performance. In this work, we propose a
multi-task self-supervised learning framework for computational drug
repositioning. The framework tackles label sparsity by learning a better drug
representation. Specifically, we take the drug-disease association prediction
problem as the main task, and the auxiliary task is to use data augmentation
strategies and contrast learning to mine the internal relationships of the
original drug features, so as to automatically learn a better drug
representation without supervised labels. And through joint training, it is
ensured that the auxiliary task can improve the prediction accuracy of the main
task. More precisely, the auxiliary task improves drug representation and
serving as additional regularization to improve generalization. Furthermore, we
design a multi-input decoding network to improve the reconstruction ability of
the autoencoder model. We evaluate our model using three real-world datasets.
The experimental results demonstrate the effectiveness of the multi-task
self-supervised learning framework, and its predictive ability is superior to
the state-of-the-art model. | | Source: | arXiv, 2206.00262 | | Services: | Forum | Review | PDF | Favorites | |
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