Subcellular Localization with Machine Learning Methods

Basak S., BOSTANCI G. E.

6th International Conference on Problems of Cybernetics and Informatics, PCI 2025, Baku, Azerbaycan, 25 - 28 Ağustos 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/pci66488.2025.11219658
  • Basıldığı Şehir: Baku
  • Basıldığı Ülke: Azerbaycan
  • Anahtar Kelimeler: K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron (MLP), Naive Bayes, Random Forest, Subcellular localization, Support Vector Machine (SVM)
  • Ankara Üniversitesi Adresli: Evet

Özet

The subcellular localization of proteins plays a vital role in determining their biological function, regulation and interactions in the cellular environment. Given its critical importance in cellular metabolism, subcellular localization prediction has become an important research area in artificial intelligence and deep learning. Recent advances enable the classification of protein locations within the cell with high prediction accuracy using various machine learning algorithms. In the study subcellular localization of proteins was classified using The Human Protein Atlas dataset. Multiple supervised learning algorithms including Logistic Regression, Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multilayer Perceptron (MLP) Neural Networks were used. The experimental results show that the machine learning methods used in the study are found successful in the study scope.