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Life and Human Information, Health and Medicine Lab
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The lab uses AI to integrate health and medical data and study everything from molecules to whole-body movement.
AboutAbout the lab
Reading health through multiple layers of data
The Life and Human Information, Health and Medicine Lab combines computational biology and digital health technologies to pioneer data-driven health science.
In the human body, molecular activity (micro) and body movement (macro) influence each other.
The lab aims to use AI to integrate data from both layers and build precise digital twins for medicine.
In computational biology, it uses omics analysis covering genomes, proteins, glycans, and more. In digital health, it uses sensing with 3D cameras and wearable devices.
On the lab's website, it is introduced under the name "Matsui Lab."
Key terms
- Omics
- A general term for fields that comprehensively measure and study biological molecules such as the genome, proteins, and glycans
- Glycan
- A molecule made of sugars linked in a chain. The field that studies glycans as a whole is called glycomics
- Digital twin
- A computer-based counterpart that captures the state of the body as data
ResearchResearch themes
The lab aims to pioneer data-driven health science by combining computational biology and digital health technologies. It integrates and investigates data from multiple layers, from molecular networks to whole-body sensing.
*Illustrative imageHow readily each tissue can build glycans
Glycan synthesis requires the coordinated expression of genes such as glycosyltransferases. By combining expression levels at each step of the pathway, this approach produces a score that can be compared across tissues and identifies the steps where expression is the limiting factor.
*Illustrative imageAn AI model that learns glycans
This model represents glycans as graphs of monosaccharides and their linkages, and is pretrained with a graph transformer. It outperforms existing methods on benchmark tasks and also suggests candidates for sequences whose notation is ambiguous.
*Illustrative imageA resource linking glycans and medical terms
To make sets of glycans biologically interpretable, the lab built a resource that links glycans to Medical Subject Headings (MeSH). It consists of an inference model, a mapping database, and an enrichment analysis workflow.
*Illustrative imageAnalyzing variation in glycan structure
This is an R package that reads the output of glycoprotein analysis software and extracts structural features of glycans that are easy to interpret. It quantifies variation at the site and protein levels and compares conditions.
MembersFaculty
- Associate ProfessorYusuke Matsui
- Assistant ProfessorKoichi Himori
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Janet Thornton giving a talk at the European Conference on Computational Biology 2012 in Basel” University of Basel / CC BY 3.0 (cropped)
- How readily each tissue can build glycans: “Glycosylation of a polypeptide” Kep17 / CC BY-SA 4.0 (cropped)
- An AI model that learns glycans: “Message Passing Neural Network” NickDiCicco / CC BY-SA 4.0 (cropped)
- A resource linking glycans and medical terms: “Schlagwortkatalog” Dr. Marcus Gossler / CC BY-SA 3.0 (cropped)
- Analyzing variation in glycan structure: “Protein Imager molecular rendering example 3 CsgFG complex” Molpgraphfan / CC BY-SA 4.0 (cropped)
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(作業療法科学): “Occupational Therapy session” sabreguy29 / CC BY 2.0 (cropped) - Related lab (生体分子情報科学): “Agilent 1200HPLC and 6130 MS” Nick Birse / CC BY-SA 4.0 (cropped)
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