Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots-Reference-Cited by-同舟云学术

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

Published:2016 Issue:9 Volume:12 Page:2893-2900
ISSN:1742-206X
Container-title:Molecular BioSystems
language:en
Short-container-title:Mol. BioSyst.

Author:

Dong Chuan¹²³⁴⁵,Yuan Ya-Zhou¹²³⁴⁵,Zhang Fa-Zhan¹²³⁴⁵,Hua Hong-Li¹²³⁴⁵,Ye Yuan-Nong⁶⁷⁸⁵,Labena Abraham Alemayehu¹²³⁴⁵,Lin Hao¹²³⁴⁵,Chen Wei⁹¹⁰¹¹¹²¹³,Guo Feng-Biao¹²³⁴⁵

Affiliation:

1. Center of Bioinformatics

2. School of Life Science and Technology

3. University of Electronic Science and Technology of China

4. Chengdu

5. China

6. School of Biology and Engineering

7. Guizhou Medical University

8. Guiyang

9. Department of Physics

10. School of Sciences

11. Center for Genomics and Computational Biology

12. North China University of Science and Technology

13. Tangshan

Abstract

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics.

Publisher

Royal Society of Chemistry (RSC)

Subject

Molecular Biology,Biotechnology

Link

http://pubs.rsc.org/en/content/articlepdf/2016/MB/C6MB00374E

Reference37 articles.

1. Efficient Inference of Recombination Hot Regions in Bacterial Genomes

2. Comparative Recombination Rates in the Rat, Mouse, and Human Genomes

3. Widely Conserved Recombination Patterns among Single-Stranded DNA Viruses

4. A Hierarchical Combination of Factors Shapes the Genome-wide Topography of Yeast Meiotic Recombination Initiation

5. Global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae

Cited by 21 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. The Prediction of Recombination Hotspot Based on Automated Machine Learning;Journal of Molecular Biology;2024-06

2. Identify essential genes based on clustering based synthetic minority oversampling technique;Computers in Biology and Medicine;2023-02

3. Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model;Methods;2023-02

4. Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning;Intelligent Automation & Soft Computing;2023

5. m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties;Analytical Biochemistry;2020-11