Author

Kazuo Miyazawa

RIKEN Center for Integrative Medical SciencesORCID 0000-0002-1453-9037Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Assay:GWAS summary statistics· 2 Organism:human· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Approach:deep learning· 1 Assay:imaging· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

Kaoru ItoiD· 3 shared Hiroshi AkazawaiD· 2 shared Hiroshi Matsunaga· 2 shared Hirotaka IekiiD· 2 shared Issei KomuroiD· 2 shared Koichi MatsudaiD· 2 shared Kouichi OzakiiD· 2 shared Satoshi KoyamaiD· 2 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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Curved chords = mutual co-authorship

3 shared papersGenetic & Genomic Medicine

Kaoru ItoORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Genetic & Genomic MedicinemedRxivPreprintApr 27, 2026

Genetic liability to metabolic dysfunction modelled in early adulthood predicts cardiometabolic risk across the life course in Asian populations

Hong Pan, Evelyn Lau, Yurui Chen…·Dennis Y.Q. Wang

This paper develops a polygenic score for metabolic syndrome in early adulthood and shows it predicts cardiometabolic risk across the life course in Asian populations.

DOI 10.64898/2026.04.24.26351660

humanGWAS summary statistics

𝕏 PostBluesky

Genetic & Genomic MedicinemedRxivPreprintSep 6, 2021

Trans-ancestry genome-wide analysis of atrial fibrillation provides new insights into disease biology and enables polygenic prediction of cardioembolic risk

Kazuo Miyazawa, Kaoru Ito, Zhaonan Zou…·Issei Komuro

This paper identifies new genetic loci for atrial fibrillation through a trans-ancestry meta-analysis and develops a polygenic risk score linked to cardioembolic risk.

DOI 10.1101/2021.09.06.21263189

humanGWAS summary statistics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMar 24, 2021

Deep learning-based chest X-ray age serves as a novel biomarker for cardiovascular aging

Hirotaka Ieki, Kaoru Ito, Mike Saji…·Issei Komuro

This paper develops a deep learning model to estimate patient age and sex from chest X-rays, finding that CXR age correlates with cardiovascular aging and disease outcomes.

5 citations

imagingdeep learning

𝕏 PostBluesky