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342003

Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches

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Last updated: 29 Dec 2024

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Abstract

This study investigates antimicrobial peptides (AMPs), pivotal in combating infections, using accessible machine learning methods. We examined long, medium, and short peptides, focusing on specific features. Initially, supervised classification, guided by a reference paper from fellow researchers in our department, was employed to analyze peptides across several features. This approach provided insights into the effectiveness of these peptides. Subsequently, we adopted unsupervised learning techniques, utilizing tools such as SVM (Support Vector Machines), RF (Random Forest), and KNN (K-Nearest Neighbors). Our findings unveil new insights into the peptides, revealing both anticipated and unexpected patterns. While the supervised approach helped us understand the known characteristics, unsupervised learning allowed for the discovery of hidden analogies and patterns not considered by traditional chemical analysis. This work is significant as it deepens our comprehension of AMPs, paving the way for improved treatments for infections. The study underscores the synergy between machine learning and biochemical insights in the exploration of peptide functionality.

DOI

10.21608/iiis.2024.342003

Keywords

Antimicrobial Peptides (AMPs), Machine Learning Techniques, Biological Insights, Biochemical analysis, Therapeutic Peptides, Supervised vs. unsupervised learning, iLearn Plus tool, Protein sequence analysis, Biomedical Research

Authors

First Name

Ahmed

Last Name

El-Gabry

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Affiliation

Deptment of Healthcare Engineering Fuclty of Enginering Cairo University Giza , Egypt

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First Name

Antonious

Last Name

Atef Saleh

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Affiliation

Deptment of Healthcare Engineering Fuclty of Enginering Cairo University Giza , Egypt

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First Name

Omar

Last Name

El Saeed

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-

Affiliation

Deptment of Healthcare Engineering Fuclty of Enginering Cairo University Giza , Egypt

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-

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Volume

1

Article Issue

1

Related Issue

46204

Issue Date

2024-02-01

Receive Date

2024-02-19

Publish Date

2024-02-01

Page Start

8

Page End

15

Online ISSN

2682-258X

Article File

IIIS_Volume 1_Issue 1_Pages 8-15.pdf

PDF . 664.5kB

Link

https://iiis.journals.ekb.eg/article_342003.html

Detail API

https://iiis.journals.ekb.eg/service?article_code=342003

Order

342,003

Type

Original Article

Type Code

3,047

Publication Type

Journal

Publication Title

International Integrated Intelligent Systems

Publication Link

https://iiis.journals.ekb.eg/

MainTitle

Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches

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Type

Article

Created At

21 Dec 2024