TY  - JOUR
T1  - High Resolution Frequency Estimation by Minimum Norm Solution for Effective Gene Prediction
AU - Barman, S. AU - Roy, M. 
JO  - Journal of Engineering and Applied Sciences
VL  - 8
IS  - 6
SP  - 198
EP  - 207
PY  - 2013
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2013.198.207
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2013.198.207
KW  - Periodogram
KW  -de-oxyribo nucleic acid
KW  -minimum norm solution
KW  -eigen-vector
KW  -eigen value
AB  - The recent techniques of spectrum estimation are based on 
  linear algebraic concepts of subspaces. In this study, the researchers have 
  used noise subspace method for finding hidden periodicities in DNA. With the 
  vast growth of genomic sequences, the demand to identify accurately the protein 
  coding regions in DNA is increasingly rising. In the past, several techniques 
  involving various cross-fields have come up, among which application of digital 
  signal processing tools is of prime importance. It is known that coding segments 
  have a 3-base periodicity while non-protein coding regions do not have this 
  unique feature. One of the most important spectrum analysis technique based 
  on the concept of subspace is the minimum norm method. The minimum norm estimator 
  developed in this study shows sharp period-3 peaks in coding regions completely 
  eliminating background noise. Comparison of proposed method with existing Sliding 
  Discrete Fourier Transform (SDFT) method popularly known as periodogram has 
  been drawn on several genes from various organisms showing that the proposed 
  method has effective approach towards gene prediction. Resolution, quality factor, 
  sensitivity, specificity, miss rate, wrong rate and computation time are used 
  to establish superiority of minimum norm gene prediction method over existing 
  method.
ER  -