TY - JOUR
T1 - Deep metabolome
T2 - Applications of deep learning in metabolomics
AU - Pomyen, Yotsawat
AU - Wanichthanarak, Kwanjeera
AU - Poungsombat, Patcha
AU - Fahrmann, Johannes
AU - Grapov, Dmitry
AU - Khoomrung, Sakda
N1 - Publisher Copyright:
© 2020 The Author(s)
PY - 2020/1
Y1 - 2020/1
N2 - In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
AB - In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
KW - Artificial neural network
KW - Deep learning
KW - Mass spectrometry
KW - Metabolomics
KW - NMR
UR - http://www.scopus.com/inward/record.url?scp=85092695235&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092695235&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2020.09.033
DO - 10.1016/j.csbj.2020.09.033
M3 - Review article
C2 - 33133423
AN - SCOPUS:85092695235
SN - 2001-0370
VL - 18
SP - 2818
EP - 2825
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -