Prediction of donor splice sites using random forest with a new sequence encoding approach
KRISHI: Publication and Data Inventory Repository
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Title |
Prediction of donor splice sites using random forest with a new sequence encoding approach
Not Available |
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Creator |
Prabina Kumar Meher
Tanmaya Kumar Sahu Atmakuri Ramakrishna Rao |
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Subject |
donor splice site
Homo sapiens SpliceView MEM |
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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. Not Available |
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Date |
2022-08-07T05:22:26Z
2022-08-07T05:22:26Z 2016-01-22 |
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Type |
Research Paper
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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
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/73727 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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