©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.
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.
FULL-TEXT (SUBSCRIBE OR
PURCHASE TITLE)