Yao-zhong Zhang @ IMSUT

Yao-zhong Zhang (張耀中)

Associate Professor
Division of Health Medical Intelligence, Human Genome Center,
Institute of Medical Science, the University of Tokyo

[Address]: 8th floor, General Research Building,
Shirokanedai 4-6-1, Minato-ku, Tokyo 108-8639 JAPAN.
[Phone]: (+81)03-5449-5617
[Email]: yaozhong [AT] ims.u-tokyo.ac.jp ; yaozhong [AT] hgc.jp

[Github] [DockerHub] [JP_HP]

I am an associate professor at Division of Health Medical Intelligence, the Institue of Medical Science,
the University of Tokyo. We have wide research interests in health and aging related topics based on huge omics data.
My research goal aims to contribute to solving real-world healthcare challenges through better utilizing advanced AI techniques.

Research Interests

- Developing deep learning models for genomic data analysis

- Bioinformatics for long read sequencing (Nanopore basecalling and methylation detection)

- Genomic profiles/representations (Genomic language models for human, phage, bacteria genomes) [!] Our group are searching self-motivated Phd students and PostDocs. Feel free to contact me:)

Preprint

Tissue-Specific Cell Type Annotation with Supervised Representation Learning using Split Vector Quantization and Its Comparisons with Single-cell Foundation Models
Yusri Dwi Heryanto, Yao-zhong Zhang, Seiya Imoto
BioAxiv

Rethinking Representation Complexity in Drug–Target Prediction via Supervised Vector Quantization
Jiandong Chen, Yao-zhong Zhang, Lu Lu, Meixi Wu, Zhiang Chen, Seiya Imoto, Chen Li
BioAxiv

Publication

Diffusion model for imputing time-series gut microbiome profiles using phylogenetic information and metadata integration
Misato Seki, Yao-zhong Zhang, Seiya Imoto.
Bioinformatics Advances, Accepted

PharaCon: A new framework for identifying bacteriophages via conditional representation learning
Zeheng Bai, Yao-zhong Zhang, Yuxuan Pang, Seiya Imoto
Bioinformatics, 2025

TDFPS-Designer: an efficient toolkit for barcode design and selection in nanopore sequencing
Renmin Han, Junhai Qi, Zhengyi Li, Yao-zhong Zhang, Guojun Li, Xin Gao
Genome biology, 2024

Genome analysis through image processing with deep learning models
Yao-zhong Zhang, Seiya Imoto
Journal of Human Genetics, Review, 2024

biotextgraph: graphical summarization of functional similarities from textual information
Noriaki Sato, Yao-zhong Zhang, Zuguang Gu, Seiya Imoto
Bioinformatics, Volume 40, Issue 6, 2024

Predicting cell types with supervised contrastive learning on cells and their types
Yusri Dwi Heryanto, Yao-zhong Zhang, Seiya Imoto.
Scientific reports, 14, Article number: 430 (2024).

Imputing time-series microbiome abundance profiles with diffusion model
Misato Seki, Yao-zhong Zhang, Seiya Imoto.
BIBM 2023, Oral (short).

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings
Yao-zhong Zhang, Zeheng Bai, Seiya Imoto.
Bioinformatics, 2023

Zero-shot-capable identification of phage–host relationships with whole-genome sequence representation by contrastive learning
Yao-zhong Zhang, Yunjie Liu, Zeheng Bai, Kosuke Fujimoto, Satoshi Uematsu, Seiya Imoto.
Briefings in Bioinformatics, 2023

Identification of Bacteriophages Using Deep Representation Model with Pre-training
Zeheng Bai, Yao-zhong Zhang, Satoru Miyano, Rui Yamaguchi, Satoshi Uematsu, Seiya Imoto.
Bioinformatics, Volume 38, Issue 18. 2022

On the application of BERT models for nanopore methylation detection
Yao-zhong Zhang, Sera Hatakeyama, Kiyoshi Yamaguchi, Yoichi Furukawa, Satoru Miyano, Rui Yamaguchi, Seiya Imoto.
BIBM 2021.Oral.

Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers
Yao-zhong Zhang, Seiya Imoto, Satoru Miyano, Rui Yamaguchi.
PLOS Computational Biology 17(10): e1009186(2021).

Nanopore base-calling from a perspective of instance segmentation
Yao-zhong Zhang, Arda Akdemir, Georg Tremmel, Seiya Imoto, Satoru Miyano, Tetsuo Shibuya, Rui Yamaguchi.
BMC Bioinformatics 21, 136 (2020).

Reconstruction of high read-depth signals from low-depth whole genome sequencing data using deep learning
Yao-zhong Zhang, Seiya Imoto, Satoru Miyano, Rui Yamaguchi.
BIBM 2017. DLB2H workshop

Sequence-specific Bias Correction for RNA-seq Data using Recurrent Neural Networks
Yao-zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BMC Genomics volume 18, Article number: 1044 (2017)

Structure-guided Supertagger Learning.
Yao-zhong Zhang, Takuya Matsuzaki, Jun'ichi Tsujii
Natural Language Engineering, Volume 18, 2012