Construction of an Integrated Analysis Platform for Vision and Omics

Construction of an Integrated Analysis Platform for Vision and Omics

In life science and medical research, both image data (Vision)—such as microscopic and pathological images—and omics data (Omics)—such as gene expression levels and protein abundance—are often used.

Microscopic and pathological images provide information about cellular and tissue morphology and spatial structure, whereas omics data comprehensively captures molecular-level states such as genes and proteins. These data types describe biological systems from different perspectives, and integrating them enables a deeper understanding of biological phenomena.

In recent years, advances in technologies such as spatial transcriptomics have made it possible to obtain gene expression data along with spatial information within tissues, leading to active research on the integrated analysis of image and molecular data. However, because image data and omics data differ significantly in format and dimensionality, effectively integrating them is not straightforward.

In this research, we develop methods for integrated analysis of image and omics data using image analysis techniques and machine learning. For example, by associating morphological features extracted from tissue images with gene expression data, we aim to deepen the understanding of cellular states and disease mechanisms. Through such integrated analysis of Vision and Omics, we seek to generate new insights that contribute to our understanding of diseases and to research on diagnosis and treatment.