ABSTRACT

The identification of bacteria plays an essential role in multiple areas of research. Those areas include experimental biology, food and water industries, pathology, microbiology, and evolutionary studies. Although there exist methodologies for identification, a transition to a whole-genome sequence-based taxonomy is already undergoing. Next-Generation Sequencing helps the transition by producing DNA sequence data efficiently. However, the rate of DNA sequence data generation and the high dimensionality of such data need faster computer methodologies. Machine learning, an area of artificial intelligence, has the ability to analyze high dimensional data in a systematic, fast, and efficient way. Therefore, we propose a sequential deep learning model for bacteria identification. The proposed neural network exploits the vast amounts of information generated by Next-Generation Sequencing, in order to extract an identification model for whole-genome bacteria sequences. After validating the identification model, the bidirectional recurrent neural network outperformed other classification approaches.

摘要

细菌的鉴定在多个研究领域中起着至关重要的作用。这些领域包括实验生物学、食品和水工业、病理学、微生物学和进化研究。尽管存在识别方法，但向基于全基因组序列的分类法的转变已经在进行中。下一代测序通过有效地生成 DNA 序列数据来帮助过渡。然而，DNA 序列数据生成的速度和此类数据的高维度需要更快的计算机方法。机器学习是人工智能的一个领域，具有系统、快速、高效地分析高维数据的能力。因此，我们提出了一种用于细菌识别的顺序深度学习模型。所提出的神经网络利用下一代测序产生的大量信息，以提取全基因组细菌序列的识别模型。在验证识别模型后，双向循环神经网络优于其他分类方法。