2023-2034 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking: To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


OJBTM

 Online Journal of Bioinformatics  

Established 1995

ISSN  1443-2250

 

 Volume 24 (2):92-101, 2023.


Cognition enhanced artificial neural network for detection of rare DNA.

 

Meena K1, Menaka K2, Sundar TV4, Subramanian KR3

 

1Bharathidasan University, 2Department of I.T. & Applications, Shrimati Indira Gandhi College, 3Department of M.C.A., Shrimati Indira Gandhi College, 4Post Graduate and Research Department of Physics, National College, Tiruchirappalli, India.

ABSTRACT

 

Meena K, Menaka K, Sundar TV, Subramanian KR., Cognition enhanced artificial neural network for detection of rare DNA., Onl J Bioinform., 24 (2):92-101, 2023. Looking for rare variations of genetic codes between intra or inter DNA sequences can reveal disease sequences. Neural networks enhanced by preprocessed input data may boost cognition processing. Skewness of nucleotide bases in training set, sequences and profile were spread zigzag profile -0.02 to 0.17 for cognition enhancement during training phase. For performance of training and detection by neural network, mean square errors in output after back propagating weights were determined at specific iterations for rare sequences. This network was capable of rapid cognition and as well gives relatively better detection performance when compared to conventional learning methods.

 

Keywords: Knowledge driven Artificial Neural Networks, DNA sequences, Numerical Characterization, Skewness.


MAIN

 

FULL-TEXT (SUBSCRIBE OR PURCHASE TITLE)