早稲田大学 · Biochemistry, Genetics and Molecular Biology
Michiaki Hamada 교수의 연구실은 RNA 생물학과 인공지능 기반 생물정보학을 융합한 연구를 주도하고 있습니다. 특히 비코딩 RNA(예: lncRNA)의 기능 예측과 RNA-RNA 상호작용을 정밀하게 예측하는 소프트웨어 도구(예: LncRRIsearch, CentroidFold) 개발에 주력하고 있으며, SELEX 실험 데이터를 기반으로 한 인공지능 기반 아pto머 생성 기술(RaptGen)도 개발했습니다. 연구는 주로 생물학적 기능을 해석하고 신약 타겟을 탐색하는 데 초점을 맞추고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.
Supplementary data are available at Bioinformatics online.
Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed "LncRRIsearch," which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA in
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). Various candidates are limited by actual sequencing data from an experiment. Here we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimensiona
Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classificati
Supplementary data are available at <i>Bioinformatics Advances</i> online.
Aptamers are short single-stranded RNA/DNA molecules that bind to specific target molecules. Aptamers with high binding-affinity and target specificity are identified using an in vitro procedure called high throughput systematic evolution of ligands by exponential enrichment (HT-SELEX). However, the development of aptamer affinity reagents takes a considerable amount of time and is costly because HT-SELEX produces a large dataset of candidate sequences, some of which have insufficient binding-af
It has been recently suggested that transposable elements (TEs) are re-used as functional elements of long non-coding RNAs (lncRNAs). This is supported by some examples such as the human endogenous retrovirus subfamily H (HERVH) elements contained within lncRNAs and expressed specifically in human embryonic stem cells (hESCs), as required to maintain hESC identity. There are at least two unanswered questions about all lncRNAs. How many TEs are re-used within lncRNAs? Are there any other TEs that
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
The software is available upon request.
This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.
