Pathogenicity Prediction of Gene Fusion in Structural Variations: A Knowledge Graph-Infused Explainable Artificial Intelligence (XAI) Framework.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important.

Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs.

Background: To treat diseases caused by genetic variants, it is necessary to identify disease-causing variants in patients. However, since there are a large number of disease-causing variants, the application of AI is required. We propose AI to solve this problem and report the results of its application in identifying disease-causing variants.

Hypersensitivity pneumonitis induced by the spore of Pleurotus Eryngii (Eringi).

We reported the first case of hypersensitivity pneumonitis (HP) by an edible mushroom, Pleurotus Eryngii (Eringi). A 54-year-old woman had worked in a Bunashimeji mushroom factory for 42 months, and she moved to a new factory producing Eringi. Two months after, she was found to have HP by the spore of Eringi.