Prediction of plant secondary metabolic pathways using deep transfer learning.

ABSTRACT:

Background

Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways.

Results

GTC provides comprehensive molecular representation by extracting both structural features from the mo

SUBMITTER: Bao H 

PROVIDER: S-EPMC10507959 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature