To date, there are no effective treatments for most neurodegenerative diseases. However, certain foods may be associated with these diseases and bring an opportunity to prevent or delay neurodegenerative progression. Our objective is to construct a k
nowledge graph for neurodegenerative diseases using literature mining to study their relations with diet. We collected biomedical annotations (Disease, Chemical, Gene, Species, SNP&Mutation) in the abstracts from 4,300 publications relevant to both neurodegenerative diseases and diet using PubTator, an NIH-supported tool that can extract biomedical concepts from literature. A knowledge graph was created from these annotations. Graph embeddings were then trained with the node2vec algorithm to support potential concept clustering and similar concept identification. We found several food-related species and chemicals that might come from diet and have an impact on neurodegenerative diseases.
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sample a subset of the action space that provides varying mixtures of ex
ploitation and exploration for inclusion in a search tree. The proposed method first evaluates the action space according to a score function that is a linear combination of expected reward and expected information gain. The actions with the highest score are then added to the search tree during tree expansion. Experiments show that PA-POMCPOW is able to outperform existing state-of-the-art solvers on problems with large discrete action spaces.
