Deep Tech

Realizing Precision Medicine Through Molecular Profiling

Molecular cohort studies involve the long-term collection and analysis of molecular-level data—such as genetic information, proteins, and metabolites—from specific groups (cohorts). While traditional cohort studies focused on lifestyle habits and environmental factors, molecular cohort studies incorporate “individual differences at the molecular level.” This approach plays a significant role in elucidating disease mechanisms and advancing preventive medicine. This research design is utilized to build predictive models for lifestyle-related diseases such as cancer, cardiovascular disease, and diabetes, and to discover disease biomarkers. It is a crucial method for realizing Precision Medicine. As a next-generation research platform, it promotes studies directly linked to optimizing disease prevention strategies based on molecular profiles and identifying novel drug targets.

The Evolution of Understanding Living Systems as Systems Biology

Omics analysis aims to understand biological phenomena at the systems level by comprehensively and quantitatively analyzing molecular groups within living organisms. Omics analysis technologies, based on molecular biology and bioinformatics, have advanced dramatically in recent years due to technological progress in next-generation sequencing (NGS) and mass spectrometry (MS).

• Genomics
  Comprehensively analyze genetic variants, copy number variations (CNVs), structural variations, and more using whole genome sequencing (WGS) and whole exome sequencing (WES).
•  Epigenomics
  Comprehensively assesses DNA methylation, histone modifications, and chromatin accessibility.
•  Transcriptomics
  Comprehensively analyze transcripts to identify gene expression profiles and splicing variants.
•  Proteomics
  Elucidate disease-specific biomarkers and drug response mechanisms through mass spectrometry-based analysis of protein expression levels, post-translational modifications, and interaction networks.
• Metabolomics
  Comprehensive analysis of low-molecular-weight metabolites using NMR and MS.
  Understanding the dynamics of metabolic pathways and the homeostasis of the entire system.

By combining these omics data, they are integrated and utilized as multi-omics analysis, contributing to elucidating disease mechanisms and optimizing prevention and treatment strategies.

Next-generation foundational technology to overcome the complexity of big data

Genome, transcriptome, proteome, and metabolome analyses generate multidimensional and high-dimensional data. This data contains a large number of variables, making interpretation difficult using conventional statistical methods alone. Artificial intelligence (AI), particularly machine learning and deep learning, is being utilized as a powerful analytical tool. These technologies are expected to accelerate disease prediction, personalized medicine, and drug discovery research. Applications include predicting drug sensitivity and side effects from patient-derived omics data, and simulating treatment scenarios by recreating individual molecular profiles.