Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classification
Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classification
复制标题
通过鸟枪法宏基因组序列分类进行病原体检测的深度学习方法
DOI：
10.1007/978-3-030-21642-9_4
复制
发表时间：
2019
影响因子：
7.5
通讯作者：
Nakamura Shota
中科院分区：
文献类型：
--
作者：
Hsu Ying-Feng;Ito Makiko;Maruyama Takumi;Matsuoka Morito;Jung Nicolas;Matsumoto Yuki;Motooka Daisuke;Nakamura Shota
研究表明，霰弹枪宏基因组测序有助于评估各种病毒、细菌和真核微生物，并有助于在复杂样品中探索它们的丰度。由于处理大量序列和总体计算复杂性的挑战，通过传统的数据库序列比较方法分析这些数据非常耗时。深度学习已被广泛用于解决许多分类问题，包括生物信息学领域的分类问题，并已证明其在分析大规模数据集方面的准确性和效率。这项工作的目的是探索如何使用长短期记忆（LSTM）网络通过从宏基因组数据中检测病原体来学习序列基因组模式。我们的实验结果表明，我们可以获得与传统BLAST方法相似的精度，但速度要快36倍左右。
Studies have shown that shotgun metagenomics sequencing facilitates the evaluation of diverse viruses, bacteria, and eukaryotic microbes and assists in exploring their abundances in complex samples. Due to the challenges of processing a substantial amount of sequences and overall computational complexity, it is time-consuming to analyze these data through traditional database sequence comparison approaches. Deep learning has been widely used to solve many classification problems, including those in the bioinformatics field, and has demonstrated its accuracy and efficiency for analyzing large-scale datasets. The purpose of this work is to explore how a long short-term memory (LSTM) network can be used to learn sequential genome patterns through pathogen detection from metagenome data. Our experimental result showed that we can obtain similar accuracy to the conventional BLAST method, but at a speed that is about 36 times faster.
影响因子：
5.6
作者：
ALTSCHUL, SF;GISH, W;LIPMAN, DJ
通讯作者：
LIPMAN, DJ