<?xml version="1.0" encoding="UTF-8"?>
<records>
<record>
<language>eng</language>
<publisher>International Academy of Ecology and Environmental Sciences</publisher>
<journalTitle>Network Biology</journalTitle>
<issn>2220-8879</issn>
<publicationDate>2022-12-1</publicationDate>
<volume>12</volume>
<issue>4</issue>
<startPage>133</startPage>
<endPage>141</endPage>
<doi> </doi>
<publisherRecordId>1</publisherRecordId>
<documentType>article</documentType>
<title language="eng">Evolutionary aspect of protein sequence network based on the 2D
 representation of amino acids</title>
<authors>
<author>
<name>Sanjay Sharma</name>
<email></email>
<affiliationId>1</affiliationId>
<affiliationId>2</affiliationId>
</author>
<author>
<name>Birinchi Kumar Boruah</name>
<email></email>
<affiliationId>1</affiliationId>
<affiliationId>2</affiliationId>
</author>
<author>
<name>Tazid Ali</name>
<email></email>
<affiliationId>1</affiliationId>
<affiliationId>2</affiliationId>
</author>
</authors>
<affiliationsList>
<affiliationName affiliationId="1">
Department of Mathematics, Dibrugarh University, Assam, India 786004
</affiliationName>
</affiliationsList>
<abstract>
For the comparative analysis of proteins, their proper clustering, and evolutionary relationships require analysis of their sequences. We used a mathematical parameter termed a similar factor to create a similar degree matrix of ND6 protein sequences taken from eight different species in this paper. We built a network out of the matrix to analyze their evolutionary and similarity trends with each other. By observing the various centrality measures, the correlation between multiple centrality measures and different network parameter shows that our network is consistent with the known evolution fact of ND6 protein sequences.
</abstract>
<fullTextUrl format="pdf">
http://www.iaees.org/publications/journals/nb/articles/2022-12(4)/protein-sequence-network-based-on-2D-representation.pdf
</fullTextUrl>
<keywords>
<keyword>amino acids</keyword>
<keyword>similar factor</keyword>
<keyword>similar degree matrix</keyword>
<keyword>characteristic vector</keyword>
<keyword>centrality measures</keyword>
<keyword>clustering coefficient</keyword>
</keywords>
</record>
</records>
