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All Times EDT

Thursday, June 4
Machine Learning
Software & Data Science Technologies
Machine Learning and Software and Data Science Technologies Posters
Thu, Jun 4, 2:00 PM - 5:00 PM
TBD
 

Multiple Sequence Alignment Using Tensor Analysis (308365)

Raj Acharya, Indiana University Bloomington 
*Mian Arif Shams Adnan, Bowling Green State University 

Keywords: Chi-square test, Transition Probability

The existing pair-wise sequence alignments suffer from either deciding the appropriate gap penalty or not ensuring more accuracy. The current author inaugurated alternative methods for pair-wise and multiple sequence alignment which do not depend on gap penalty rather the differences between transition probabilities. Several test statistics have been developed for the multiple sequence alignment based on transition volume matrices of several features (like nucleotides, angling, bonding, etc) of sequences.