Data Science for Life Sciences


Welcome to Sato Laboratory for Biomedical Data Science, School of Life Science and Technology, Tokyo Institute of Technology.

Our research interest includes bioinformatics as data-driven science, aiming to accelerate life science research by applying artificial intelligence and machine learning technologies.

Latest News

Aug 9, 2024
RNA secondary structure prediction servers temporarily stopped

The RNA secondary structure prediction servers (MXfold2, IPknot, RactIP) will be temporarily down due to a power shutdown for maintenance. From Friday, August 9, 2024 at approximately 13:00 UTC, until Sunday, August 11, 2024 at approximately 13:00 UTC.
Aug 1, 2024
Moved to Tokyo Tech

Prof. Sato has joined Tokyo Institute of Technology School of Life Science and Technology.
Jul 22, 2024
Benasque meeting on RNA

Prof. Sato joined Benasuque meeting on RNA and gave a talk.
Apr 1, 2024
New members joined our lab

Twelve undergraduate students and two graduate students (master’s program) who will conduct research in our laboratory have joined us. Members

May 27, 2023
A new review paper has been published in Briefings in Bioinformatics

Our new review article on RNA informatics, co-authored with Prof. Hamada, has been published in Briefings in Bioinformatics. Sato, K. & Hamada, M. Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.
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Featured Publications

RNA Secondary Structure Prediction Based on Energy Models
This chapter introduces the RNA secondary structure prediction based on the nearest neighbor energy model, which is one of the most …
Manato Akiyama, Kengo Sato
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Prediction of RNA secondary structure including pseudoknots for long sequences
RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is …
Kengo Sato, Yuki Kato
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RNA secondary structure prediction using deep learning with thermodynamic integration
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine …
Kengo Sato, Manato Akiyama, Yasubumi Sakakibara
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