PROTEIN-PROTEIN INTERACTION PREDICTION USING A DEEP NEURAL NETWORK WITH BATCH NORMALIZATION AND QUARTILE ALGORITHM
- Assistant Professor, Department of Computer Science, Esatic, Cote Divoire.
- Assistant Professor, Department of Computer Science, Esatic, Cote Divoire.
- Assistant Professor, Department of Computer Science, Una, Cote Divoire.
- Professor, Department of Computer Science, Esatic, Cote dIvoire.
Abstract
Detecting protein-protein interactions (PPIs) is key for disease therapy development. While experimental methods are costly, deep neural network (DNN) models now use available PPI data for prediction, though limited by low-quality sequence-based data. This study introduces FDPPI, a DNN model leveraging a quartile-based algorithm and batch normalization to enhance performance, achieving 98.09% accuracy, 98.34% precision, and 97.72% sensitivity on human PPI data.
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How to Cite This Article
N. Diffon Charlemagne Kopoin, Alex Armand Josue Akohoule, Wielfrid Morie and Olivier Pascal Asseu (2024); PROTEIN-PROTEIN INTERACTION PREDICTION USING A DEEP NEURAL NETWORK WITH BATCH NORMALIZATION AND QUARTILE ALGORITHM, International Journal of Advanced Research (IJAR), 12 (12), 750-760, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/20074
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