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.
Not Available |
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Creator |
Prabina Kumar Meher
Tanmaya Kumar Sahu Atmakuri Ramakrishna Rao Sant Dass Wahi |
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Subject |
machine learning techniques
novel splice variants splice site prediction |
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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. Not Available |
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Date |
2022-08-07T13:16:40Z
2022-08-07T13:16:40Z 2014-11-25 |
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Type |
Research Paper
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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
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/73745 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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