Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Imajinasi Edulib JOIV : International Journal on Informatics Visualization
- Damayanti
Unknown Affiliation
Arts Humanities Computer Science & IT Library & Information Science

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
A New Feature Extraction Approach in Classification for Improving the Accuracy of Proteins Damayanti, -; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Sutyarso, -; Susanto, Gregorius Nugroho; Megawaty, Dyah Ayu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2589

Abstract

Proteins play a vital role in life as essential macromolecules, consisting of linear heteromeric biopolymers formed by amino acids covalently bonded through peptide bonds. They contribute to cell development and bolster the body's defense mechanisms. Post-translational modification processes, such as glycosylation, are necessary for proteins to function optimally. Glycosylation involves adding sugar groups to proteins, playing a critical role in various protein folding processes. Dysregulation of protein glycosylation can lead to diseases like Alzheimer's and cancer. Manual classification of glycosylated proteins is time-consuming, necessitating a faster approach. This study aims to expedite glycosylated protein classification using novel methods like AAindex, CTD, SABLE, hydrophobicity, and PseAAC for increased accuracy, comparing them with existing approaches. The dataset comprises protein sequences sourced from the openly accessible UniProt database. Results demonstrate that glycosylated protein prediction achieved 100% accuracy, surpassing previous approaches. Several features contributed to this improvement, with Hydrophobicity making a significant contribution at 24%, and PseAAC making the most significant contribution at 40% among the five extraction methods developed.
Co-Authors Akmal Junaidi Favorisen R. Lumbanraja Ihtisar, Anisah Megawaty, Dyah Ayu Susanto, Gregorius Nugroho Sutyarso, -