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Researcher's Profile

Lecturer

Hiroki UEDA

Biological Data Science

E-mail: ueda.genome.rcast.u-tokyo.ac.jp

Office: Building 4, 121

Tel: 03-5452-5406

inner link 2018 Research book

Biography

2000.08
BSc,Mathematics, University of Victoria
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2000.10
Inter Quest Co.,Ltd.
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2001.08
Softwave Corp.
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2002.12
Metropolitan Computer Engineer Association
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2003.03
M.S, Graduate School of Engineering, Kanazawa Institute of Technology
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2006.09
Resercher,Japan Biological Informatics Consortium
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2010.04
Intec Inc.
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2013.09
PhD, School of Engineering, The University of Tokyo (UTokyo)
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2015.04
Resercher, Fujitsu Limited
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2018.03
Lecturer, RCAST, UTokyo

Research Interests

With the development of sequencing technology, electronic data yields in biology have been steadily increasing, and it is already a challenging task to process large volumes of data in a conventional method. In addition, in order to extract knowledge from multi modal big data, (ex. Multi-omics data) it is necessary to incorporate the latest Data Science technology, such as cloud computing and machine learning. Research topics include following.
1. Epitranscriptome analysis
Epitranscriptome is transcriptomics with biochemical modifications of RNA. In previous studies, we have developed a bioinformatics method to comprehensively detect inosine-modified sites in the transcriptome at the base level.
2. Cancer genomics
With using next generation sequencer (NGS), it became feasible to detect cancer somatic mutations comprehensively, and NGS is now used as clinical applications, in additions to a research use. Because the allelic fraction of a mutation depends on the tumor purity, local copy number and clonality, it is sometime difficult to call somatic mutation with high accuracy with different specimen. In previous studies, we developed algorithms to calculate somatic mutations, copy number mutations and tumor rates in cancer cells even under noisy low tumor purity conditions.
3. Bioinformatics data analysis using Data Science
In order to find the biological knowledge from biological big data, it is necessary to aggregate data on a cloud and perform distributed processing. We are developing cloud based NGS analysis pipeline using Hadoop / Spark , popular cloud computing framework, and deep learning library.

Figure3
Hepatitis B Virus (HBV) integration sites (blue) and DNA copy number break points (red) on human genome
Figure4
RNA Sequencing and Whole genome sequencing using Hadoop

Keywords

Biological Data Science, Bioinformatics, Cancer Genomics, Machine Learning

Edudational Systems

  • Department of Advanced Interdisciplinary Studies, Graduate school of Engineering

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