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Biological Data Science Ueda Laboratory

Biological Big Data to Knowledge, using Data Science

Biological Data Science

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 with conventional methods. 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. We are developing cloud based NGS analysis pipeline using Hadoop / Spark, popular cloud computing framework, and deep learning library.
Our research include following:
(1) Cancer genomics
(2) Proteomics and post translational modification
(3) epitranscriptome (RNA modifications) analysis

Hepatitis B Virus (HBV) integration sites (blue) and DNA copy number break points (red) on human genome
Hepatitis B Virus (HBV) integration sites (blue) and DNA
copy number break points (red) on human genome
RNA Sequencing and Whole genome sequencing using Hadoop
RNA Sequencing and Whole genome sequencing using Hadoop
RNA epi-transcriptome analysis using nanopore sequencer and deep learning
RNA epi-transcriptome analysis using nanopore
sequencer and deep learning

Member

  • Hiroki UEDA
  • Specialized field:Computational Biology, Cancer Genomics, Machine Learning
<As of May 2019>

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