Fish Classification Using DNA Barcode Sequences through Deep Learning Method

Symmetry

Lina Jin¹  Jiong Yu¹  Xusheng Du¹  Xiaoqian Yuan² 

[1] School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China;School of Life Science and Technology, Xinjiang University, Urumqi 830046, China;

关键词: deep learning;    fish classification;    Stacked Autoencoder;    Kernel Density Estimation;    DNA barcode;    COI gene;   

DOI  :  10.3390/sym13091599

来源: DOAJ

【 摘 要 】

Fish is one of the most extensive distributed organisms in the world. Fish taxonomy is an important component of biodiversity and the basis of fishery resources management. The DNA barcode based on a short sequence fragment is a valuable molecular tool for fish classification. However, the high dimensionality of DNA barcode sequences and the limitation of the number of fish species make it difficult to reasonably analyze the DNA sequences and correctly classify fish from different families. In this paper, we propose a novel deep learning method that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK model. In stage one, the ESK preprocesses original data from DNA barcode sequences. In stage two, EN-SAE is used to learn the deep features and obtain the outgroup score of each fish. In stage three, KDE is used to select a threshold based on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK have been validated by experiments on three datasets, with the accuracy, recall, F1-Score reaching 97.57%, 97.43%, and 98.96% on average. Those findings confirm that ESK can accurately classify fish from different families based on DNA barcode sequences.

【 授权许可】

Unknown