Abstract
Predicting microbe-disease associations is crucial for demystifying the causes of diseases and preventing them proactively. However, most of existing approaches are feeble to comprehensively investigate the interactive relationships between diseases and microbes by self-supervised manner, resulting in the microbe-disease associations are hard to ploughed. In this paper, we propose DNCL-MDA, a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> icrobe- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> isease <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ssociations prediction model based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ontrastive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning, to demystify potential microbe-disease associations (MDAs). Particularly, DNCL-MDA first constructs a pair of microbe-disease dual networks, and designs an effective information flow projection method to obtain the individual disease and microbe networks while reserving their interdependent information. Then, DNCL-MDA proposes an optimized graph contrastive learning approach to learn the discriminative feature representations of diseases and microbes. Finally, the feature representations are contacted and fed into a fully connected neural network to predict the potential microbe-disease associations automatically. Experimental results on real-world datasets demonstrate that our proposed DNCL-MDA largely outperforms the state-of-the-art methods with more promising AUC performances.
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