Prediction of donor splice sites using random forest with a new sequence encoding approach-Reference-Cited by-同舟云学术

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Prediction of donor splice sites using random forest with a new sequence encoding approach

Published:2016-01-22 Issue:1 Volume:9 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Meher Prabina Kumar,Sahu Tanmaya Kumar,Rao Atmakuri Ramakrishna

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Genetics,Molecular Biology,Biochemistry

Link

http://link.springer.com/content/pdf/10.1186/s13040-016-0086-4.pdf

Reference43 articles.

1. Sheth N, Roca X, Hastings ML, Roeder T, Krainer AR, Sachidanandam R. Comprehensive splice site analysis using comparative genomics. Nucleic Acids Res. 2006;34:3955–67.

2. Chen TM, Lu CC, Li WH. Prediction of splice sites with dependency graphs and their expanded Bayesian networks. Bioinformatics. 2005;21(4):471–82.

3. Reese MG. Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem. 2001;26(1):51–6.

4. Rajapakse J, CaH LS. Markov encoding for detecting signals in genomic sequences. IEEE Trans Comput. Biol Bioinformatics. 2005;2(2):131–42.

5. Zhang XF, Katherine HA, Ilana HC, Lawrene LS, Chasin A. Sequence Information for the Splicing of Human Pre-mRNA Identified by Support Vector Machine Classification. Genome Res. 2003;13:2637–50.

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