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Cancer System Informatics Lab
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A lab that analyzes genomic data to understand how cancer works.
AboutAbout the lab
Contributing to medicine through data science
The Cancer System Informatics Lab studies how to understand the complex systems of cancer by analyzing large-scale data such as genomic information.
As the Division of Systems Analysis at the Aichi Cancer Center Research Institute, the lab develops analysis methods using data science techniques such as deep learning and Bayesian statistical modeling.
The lab aims to use its findings to support prevention and treatment optimized for each individual.
The lab is located at Aichi Cancer Center, and students can also conduct research here as cooperative graduate students of Nagoya University Graduate School.
Key terms
- Deep learning
- A method that uses neural networks, mathematical models imitating neural circuits, to learn the features of data progressively and in depth
- Long reads
- A technology that reads long stretches of DNA at once. It is well suited to detecting large-scale structural variants and analyzing regions of repetitive sequence
- Expert panel
- In cancer genomic medicine, a meeting where doctors, pharmacists, and others discuss treatment options based on the results of panel testing
ResearchResearch themes
By analyzing large-scale data such as the genomes of cancer cells, the lab aims to understand the complex systems of cancer and to support prevention and treatment optimized for each individual. To this end, it develops analysis methods using data science techniques such as deep learning and Bayesian statistical modeling.
*Illustrative imageDistinguishing mutations with Bayesian statistics
Using Bayesian statistical modeling, the lab develops methods that separate mutations in cancer cells from sequencing errors in DNA read data and detect them with high accuracy. This technique is important for tasks such as detecting mutations from low-coverage data.
*Illustrative imageMaking DNA reading more accurate with deep learning
Long reads produced by nanopore sequencers are less accurate than short-read data, which is a challenge. Using a new deep learning model that converts electrical current values into base sequences, the lab has achieved higher accuracy than existing methods.
*Illustrative imageSupporting cancer genomic medicine with AI
To interpret the mutation information obtained from genome analysis quickly and accurately, and to help refine decision-making at expert panels, the lab studies interpretation systems that use artificial intelligence.
MembersFaculty
The members of the Cancer System Informatics Lab consist of faculty members, researchers, and research residents.
- Affiliated ProfessorRui Yamaguchi
VoicesStudent voices
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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: “Illumina MiSeq” Konrad Förstner / CC BY 2.0 (cropped)
- Distinguishing mutations with Bayesian statistics: “Human genome assembly GRCh38 chromosomes ideogram NCBI” National Center for Biotechnology Information, screenshot by Aarón Mayo / Public domain (cropped)
- Making DNA reading more accurate with deep learning: “Oxford Nanopore MinION top cropped” Cirosantilli2 / CC BY-SA 4.0 (cropped)
- Supporting cancer genomic medicine with AI: “PDC server room” Esquilo / CC BY-SA 3.0 (cropped)
- Related lab (がん分析疫学): “DNA damaged by carcinogenic 2-aminofluorene AF” Brian E. Hingerty, Oak Ridge National Laboratory Suse Broyde, New York University Dinshaw J. Patel, Memorial Sloan Kettering Cancer Center / Public domain (cropped)
- Related lab (老化基礎科学): “Shotgun sequencing lg” National Human Genome Research Institute / Public domain (cropped)
- Related lab (神経生化学): “Biochemistry Lab” Baitaal / CC BY-SA 3.0 (cropped)
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