Reliable Sequenced-Based Protein-Protein Interaction Prediction Using Lempel Ziv Complexity and Optimized Deep Learning Model
IEEE ACCESS, cilt.14, ss.103756-103775, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 14
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/access.2026.3711498
- Dergi Adı: IEEE ACCESS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Sayfa Sayıları: ss.103756-103775
- Anahtar Kelimeler: CNN-LSTM hybrid model, genetic algorithm, Lempel–Ziv complexity, protein–protein interactions
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
In this study, a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning model was developed for sequence-based prediction of protein-protein interactions (PPIs). To address the limitations of experimental methods in terms of time and cost, computational approaches were employed. A novel method based on Lempel-Ziv (LZ) complexity was proposed to select reliable non-interacting protein pairs. Protein sequences were represented as feature vectors using the Conjoint Triad (CT) method, which encodes amino acid physicochemical properties. The hybrid CNN-LSTM architecture was then used to classify interacting and non-interacting protein pairs, where CNN layers captured local sequence motifs and LSTM layers modeled long-range dependencies. Furthermore, Genetic Algorithm (GA)-based hyperparameter optimization was applied to tune model hyperparameters. The novelty of this study lies in the combination of LZ complexity-based negative sample selection, CT feature representation, and GA-optimized CNN-LSTM architecture, providing a robust and biologically informed framework for PPI prediction. The proposed model achieved 91% training accuracy and 90% testing accuracy before optimization, which increased to 94% and 93%, respectively, after GA optimization. These results demonstrate that the integrated approach enhances predictive performance and enables reliable extraction of meaningful information from protein sequences.