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A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data.

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Title A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data.
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Creator Prabina Kumar Meher
Tanmaya Kumar Sahu
Atmakuri Ramakrishna Rao
Sant Dass Wahi
 
Subject machine learning techniques
novel splice variants
splice site prediction
 
Description Not Available
Most of the approaches for splice site prediction are based on machine learning techniques. Though, these approaches provide high prediction accuracy, the window lengths used are longer in size. Hence, these approaches may not be suitable to predict the novel splice variants using the short sequence reads generated from next generation sequencing technologies. Further, machine learning techniques require numerically encoded data and produce different accuracy with different encoding procedures. Therefore, splice site prediction with short sequence motifs and without encoding sequence data became a motivation for the present study.
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Date 2022-08-07T13:16:40Z
2022-08-07T13:16:40Z
2014-11-25
 
Type Research Paper
 
Identifier Meher, P.K., Sahu, T.K., Rao, A.R. et al. (2014). A statistical approach for 5′ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics 15, 362. https://doi.org/10.1186/s12859-014-0362-6
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http://krishi.icar.gov.in/jspui/handle/123456789/73745
 
Language English
 
Relation Not Available;
 
Publisher Not Available