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.
(2024). Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches. International Integrated Intelligent Systems, 1(1), 8-15. doi: 10.21608/iiis.2024.342003
MLA
. "Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches". International Integrated Intelligent Systems, 1, 1, 2024, 8-15. doi: 10.21608/iiis.2024.342003
HARVARD
(2024). 'Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches', International Integrated Intelligent Systems, 1(1), pp. 8-15. doi: 10.21608/iiis.2024.342003
VANCOUVER
Comprehensive Machine Learning Analysis of Long and Middle Peptides: Supervised and Unsupervised Approaches. International Integrated Intelligent Systems, 2024; 1(1): 8-15. doi: 10.21608/iiis.2024.342003
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