Project/Area Number |
17F17797
|
Research Category |
Grant-in-Aid for JSPS Fellows
|
Allocation Type | Single-year Grants |
Section | 外国 |
Research Field |
Genetics/Chromosome dynamics
|
Research Institution | The University of Tokyo |
Principal Investigator |
中井 謙太 東京大学, 医科学研究所, 教授 (60217643)
|
Co-Investigator(Kenkyū-buntansha) |
BERTHIER VINCENT 東京大学, 医科学研究所, 外国人特別研究員
|
Project Period (FY) |
2017-11-10 – 2020-03-31
|
Project Status |
Granted (Fiscal Year 2019)
|
Budget Amount *help |
¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2019: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 2018: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2017: ¥400,000 (Direct Cost: ¥400,000)
|
Keywords | エンハンサー同定 / リカレントネットワーク / 細胞種特異的エンハンサー / 特徴抽出 / Optimization / Genetic Algorithm / DNA / motif finding |
Outline of Annual Research Achievements |
The past year has been dedicated to try and identify enhancer regions in the complete human genome by using recurrent neural networks. By taking the whole genome in consideration, and not just some very limited and specific regions, the hope was to reach a more comprehensive understanding of those regions and identify some as of yet unknown enhancers.The results, while significantly better than random, were not as good as hoped. State of the art enhancer identification reaches a success rate of more than 90%, but our results hovered around 60%. While it is definitely a problem that could be tackled in the future, the relatively small dataset available (in opposition to the size of the genome) made it too difficult for the current machine learning techniques to work: they require both strong ground truth and a big dataset. Sadly the hope that the available data would be enough didn't match reality.The research as since then evolved into a slightly different direction, aiming at being able to identify which enhancer is active in which cell lines. We believe that our experience for studying the motif finding problem using the genetic algorithm would be effective in this direction and thus we will explore this possibility during the remaining term.
|
Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
研究は当初計画していたより、進捗が若干遅れている。 その主な理由は、特別研究員がまったく別の分野からこの分野の研究を始めたため、いわゆる深層学習のもつゲノム配列解析能力に対する過信があったと思っている。代表者の中井はこれまでの自分の経験からいっても、若干無謀な試みではないかとアドバイスしたが、その一方でもしかするとこれまで予想もしていなかった新しい結果の緒が得られる可能性もあると思って、強く研究方向を変えるようには指導しなかった。
|
Strategy for Future Research Activity |
特別研究員はここで仕事をはじめた当初、以前から慣れ親しんでいた遺伝的アルゴリズムを使って、モチーフ抽出の問題を最初に試していた。残された時間は少ないので、この枠組を使って、細胞種特異的エンハンサーの特徴づけに取り組んでもらうこととした。残りの時間を精一杯活用して、一定の成果をあげたいと考えている。
|