Peng-Ying Wang, Zhi-Song Chen, Xiaoguo Jiao, Yun-Juan Bao
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引用次数: 0
Abstract
Group A Streptococcus (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these findings into predictive models remains challenging due to data complexity. The current study aimed to integrate deep learning models with genome-wide association study-derived genetic variants to predict pathogenic phenotypes in GAS. We evaluated the performance of several deep neural network models, including CNN, ResNet18, LSTM, and their ensemble approach in predicting GAS phenotypes. It was found that the ensemble model consistently achieved the highest prediction accuracy across phenotypes. Models trained on the full 4722-genotype set outperformed those trained on a reduced 175-genotype set, underscoring the importance of comprehensive variant data in capturing complex genotype-phenotype interactions. Performance changes in the reduced 175-genotype set compared to the full-set genotype scenarios revealed the impact of data dimensionality on model effectiveness, with CNN remaining robust, while ResNet18 and LSTM underperformed. Our findings emphasized the potential of deep learning in phenotype prediction and the critical role of data-model compatibility.
期刊介绍:
The Journal of Microbiology and Biotechnology (JMB) is a monthly international journal devoted to the advancement and dissemination of scientific knowledge pertaining to microbiology, biotechnology, and related academic disciplines. It covers various scientific and technological aspects of Molecular and Cellular Microbiology, Environmental Microbiology and Biotechnology, Food Biotechnology, and Biotechnology and Bioengineering (subcategories are listed below). Launched in March 1991, the JMB is published by the Korean Society for Microbiology and Biotechnology (KMB) and distributed worldwide.