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Predictive intelligence powered attentional stacking matrix factorization algorithm for the computational drug repositioning
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DOI：10.1016/j.asoc.2021.107633.png)
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Abstract
En 中文
Artificial Intelligence (AI) technologies are widely used to study the computational drug repositioning to find potential new uses for marketed drugs, which have a huge impact on drug development. However, due to the sparsity of datasets and the linear limitations of traditional AI or machine learning models lead to the inability of most methods to effectively obtain hidden feature of drugs and diseases To end this, Predictive Intelligence (PI) has been proposed in recent years. Hence, in this work, we propose a PI powered Attentional Stacking Matrix Factorization (PI-ASMF). Firstly, in order to extract the effective latent factor of the drugs or diseases the ASMF model superimposes the drug-disease similarity information and the drug-disease association information into the PI model, which ensures that it can learn the effective latent factor and alleviates the cold start problem to a certain extent. Subsequently, the latent factors of drugs and diseases are element-wise multiplied with their respective attentional vectors to learn the adaptive weights of each feature. Finally, the latent factors of drugs and diseases are fed into the prediction module to generate predictive values of potential drug-disease associations. The AUC and AUPR values of PI-ASMF model on two real datasets are 0.892 and 0.168, 0.913 and 0.25, respectively, which verified its superiority and validity. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Predictive intelligence (PI)
Attentional stacking matrix factorization (ASMF)
PI-ASMF
Computational drug repositioning
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