Abstract: The present invention relates to a method and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning, which may include: a whole-slide image (WSI) compression step of compressing WSI into a superpatch graph; a graph neural networks (GNN) analysis step of embedding node features and context features into the superpatch graph through a GNN model and calculating contributions for each node and edge; a biomarker acquisition step of classifying and grouping the superpatch graph according to the contributions for each node, connecting the classified and grouped superpatch graph in units of groups to generate connected graphs, normalizing and clustering features of the connected graphs, and acquiring environmental graph biomarkers for each group; and a diagnostic information extraction step of extracting and providing diagnostic information on the WSI based on the environmental graph biomarker for each group.

Abstract: The present invention relates to a method and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning, which may include: a whole-slide image (WSI) compression step of compressing WSI into a superpatch graph; a graph neural networks (GNN) analysis step of embedding node features and context features into the superpatch graph through a GNN model and calculating contributions for each node and edge; a biomarker acquisition step of classifying and grouping the superpatch graph according to the contributions for each node, connecting the classified and grouped superpatch graph in units of groups to generate connected graphs, normalizing and clustering features of the connected graphs, and acquiring environmental graph biomarkers for each group; and a diagnostic information extraction step of extracting and providing diagnostic information on the WSI based on the environmental graph biomarker for each group.

Abstract: The disclosure relates to nanopore-based technology for screening of drugs against target RNA. According to the screening method of the disclosure, drugs targeting RNA can be efficiently screened even with a very small amount sample of picomolar concentration. The discovered drugs targeting RNA can be used in the treatment of diseases associated with RNAs.

Abstract: The present invention relates to a method and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning, which may include: a whole-slide image (WSI) compression step of compressing WSI into a superpatch graph; a graph neural networks (GNN) analysis step of embedding node features and context features into the superpatch graph through a GNN model and calculating contributions for each node and edge; a biomarker acquisition step of classifying and grouping the superpatch graph according to the contributions for each node, connecting the classified and grouped superpatch graph in units of groups to generate connected graphs, normalizing and clustering features of the connected graphs, and acquiring environmental graph biomarkers for each group; and a diagnostic information extraction step of extracting and providing diagnostic information on the WSI based on the environmental graph biomarker for each group.

Abstract: The disclosure relates to nanopore-based technology for screening of drugs against target RNA. According to the screening method of the disclosure, drugs targeting RNA can be efficiently screened even with a very small amount sample of picomolar concentration. The discovered drugs targeting RNA can be used in the treatment of diseases associated with RNAs.