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

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Title Prediction of donor splice sites using random forest with a new sequence encoding approach
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Creator Prabina Kumar Meher
Tanmaya Kumar Sahu
Atmakuri Ramakrishna Rao
 
Subject donor splice site
Homo sapiens
SpliceView
MEM
 
Description Not Available
Detection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites.
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Date 2022-08-07T05:22:26Z
2022-08-07T05:22:26Z
2016-01-22
 
Type Research Paper
 
Identifier Meher, P.K., Sahu, T.K. & Rao, A.R. Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData Mining 9, 4 (2016). https://doi.org/10.1186/s13040-016-0086-4
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http://krishi.icar.gov.in/jspui/handle/123456789/73727
 
Language English
 
Relation Not Available;
 
Publisher Not Available