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LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph
Zinuo Yang1, Lei Wang1, Xiangrui Zhang1
1Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China.
Frontiers in Microbiology
|June 7, 2024
Summary
Computational models can predict microbe-drug associations, which are crucial for understanding drug efficacy and toxicity. A new model, LCASPMDA, effectively infers these associations using graph networks and ensemble strategies.
Area of Science:
- Microbiology
- Pharmacology
- Computational Biology
Background:
- The human microbiome significantly influences drug efficacy and toxicity.
- Traditional methods for identifying microbe-drug associations are costly and time-consuming.
Purpose of the Study:
- To develop an effective computational model for predicting microbe-drug associations.
- To overcome the limitations of traditional wet-lab discovery methods.
Main Methods:
- Proposed LCASPMDA model integrating a learnable graph convolutional attention network and self-paced iterative sampling ensemble strategy.
- Constructed a heterogeneous network of known microbe-drug associations.
- Employed a Multi-Layer Perceptron classifier trained with informative negative samples.
Main Results:
- LCASPMDA demonstrated superior performance in predicting microbe-drug associations compared to existing methods.
- Validation performed on two public databases: MDAD and aBiofilm.
Conclusions:
- The developed LCASPMDA model offers an efficient and accurate computational approach for microbe-drug association prediction.
- This method can accelerate the discovery of potential microbe-drug interactions, impacting drug development and personalized medicine.
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