Expectation Maximization based algorithm applied to DNA sequence motif finder-Reference-Cited by-同舟云学术

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Expectation Maximization based algorithm applied to DNA sequence motif finder

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Garbelini Jader M. Caldonazzo¹,Sanches Danilo Sipoli²,Pozo Aurora Trinidad Ramirez³

Affiliation:

1. Federal University of Paraná University of the State of Paraná Curitiba,Department of Informatics,Apucarana,Brazil,80060-000, 86813-250

2. PPGI Federal University of Technology Cornélio,Procópio,Brazil,86300-000

3. Federal University of Paraná,Department of Informatics,Curitiba,Brazil,80060-000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870216/9870201/09870303.pdf?arnumber=9870303

Reference20 articles.

1. An algorithm for finding signals of unknown length in DNA sequences

2. STREME: accurate and versatile sequence motif discovery

3. Unsupervised learning of multiple motifs in biopolymers using expectation maximization

4. The MEME Suite

5. A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling

Cited by 2 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. biomapp::chip: large-scale motif analysis;BMC Bioinformatics;2024-03-26

2. BIOMAPP::CHIP: Large-Scale Motif Analysis;2023-11-07

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