Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment

Title

Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment

Creator

Jayapriya J.; Vinay M.

Description

In this paper, deep learning algorithms are compared for aligning multiple biological molecular sequences such as DNA, RNA, and protein. Efficient algorithms are necessary for sequence alignment to identify significant insights, but there is a trade-off between time and accuracy. This study compares deep learning algorithms for multiple sequence alignment with better accuracy, using a new similarity measure to choose the best resemblance sequences in a set. Using a benchmark dataset, the algorithms compared include CNN, VAE, MLPNN, DBNs, Deep Boltzmann Machine, and GAN. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-720 LNNS, pp. 563-575.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

A comparison study; Deep learning algorithms; q-gram; Sequence alignment; TC score

Coverage

Jayapriya J., Department of Computer Science, Yeshwanthpur Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Vinay M., Department of Computer Science, Yeshwanthpur Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981993760-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-3761-5_50

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172729053&doi=10.1007%2f978-981-99-3761-5_50&partnerID=40&md5=eb065a69e1ae821d86e73d16b0e35882