代谢途径
代谢网络
计算机科学
人工智能
深度学习
有机体
模式生物
人工神经网络
机器学习
基因组
计算生物学
鉴定(生物学)
特征(语言学)
序列(生物学)
光学(聚焦)
随机森林
生物
基因
遗传学
语言学
哲学
植物
物理
光学
作者
Hayat Ali Shah,Juan Liu,Zhihui Yang,Xiaolei Zhang,Jing Feng
标识
DOI:10.1016/j.compbiomed.2022.105756
摘要
The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.
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