A Novel Method for Assessing the Statistical Significance of RNA-RNA Interactions Between Two Long RNAs-Reference-Cited by-同舟云学术

A Novel Method for Assessing the Statistical Significance of RNA-RNA Interactions Between Two Long RNAs

Published:2018-09 Issue:9 Volume:25 Page:976-986
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Fukunaga Tsukasa¹²,Hamada Michiaki²³⁴⁵⁶

Affiliation:

1. Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan.

2. Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.

4. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.

5. Institute for Medical-Oriented Structural Biology, Waseda University, Tokyo, Japan.

6. Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2017.0260

Reference39 articles.

1. A nonlinear measure of subalignment similarity and its significance levels

2. Diversifying microRNA sequence and function

3. The expanding snoRNA world

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets;Methods in Molecular Biology;2023

2. Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs;Methods in Molecular Biology;2022

3. Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs;Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2020-07-06