HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning

Abstract

Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.

Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning

“…Transfer learning allows training and prediction using the dataset from different sources with similar characteristics and significantly reduces dataset bias. Transfer learning has achieved great success in prediction tasks that require learning transfer features ( Sun et al, 2022 ; Zhu et al, 2022 ). In the field of bioinformatics, transfer learning enables existing trained models to efficiently work on similar datasets that are lack of labels, which reduces the cost of biological experiments.…”