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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Thareja, Preeti
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A novel optimized deep learning method for protein-protein prediction in bioinformatics Thareja, Preeti; Chillar, Rajender Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp749-758

Abstract

Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence-dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
Optimizing bioinformatics applications: a novel approach with human protein data and data mining techniques Thareja, Preeti; Singh Chhillar, Rajender
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2328-2337

Abstract

Biomedicine plays a crucial role in medical research, particularly in optimizing techniques for disease prediction. However, selecting effective optimization methods and managing vast amounts of medical data pose significant challenges. This study introduces a novel optimization technique, integrated bioinformatics optimization model (IBOM) for disease diagnosis, incorporating data mining to efficiently store large datasets for future analysis. Various optimization algorithms, such as whale optimization algorithm (WOA), multi-verse optimization (MVO), genetic algorithm (GA), and ant colony optimization (ACO), were compared with the proposed method. The evaluation focused on metrics like accuracy, specificity, sensitivity, precision, F-score, error, receiver operating characteristic (ROC), and false positive rate (FPR) using 5-fold cross-validation. Results indicated that the 5-fold cross-validation method achieved superior performance with metrics: 98.61% accuracy, 96.59% specificity, 88.63% sensitivity, 99.30% precision, 92.31% F-score, 10.80% error, 92.61% ROC, and a 3.00% FPR. This method was found to be the most effective, achieving an accuracy of 0.92 in disease diagnosis compared to other optimization techniques.
Co-Authors Chillar, Rajender Singh Singh Chhillar, Rajender