Faculty Member from the Software Department Publishes a Research on Deep Learning Techniques for Genetic Data Analysis

Duhaa Fadill Abbas
In the context of supporting scientific research and applying artificial intelligence techniques across multiple disciplines, Assistant Professor Dr. Sura Zaki Naji, Head of the Software Department at the College of Information Technology – University of Babylon, in collaboration with researcher Shahd Raed Hadi, published a specialized research paper in the proceedings of the International Conference on Cybersecurity and Artificial Intelligence Strategies, issued by the global publisher Springer.
The research is titled:Deep Learning Techniques for Predicting Transcription Factor Binding Sites (TFBSs) from DNA Sequencing Data
The study addresses a fundamental challenge in bioinformatics, namely the accurate identification of transcription factor binding sites within DNA sequences, which is crucial for understanding gene regulation mechanisms. The researchers proposed an advanced model that combines Convolutional Neural Networks (CNNs) with an Attention Layer, aiming to enhance the representation of genetic data and focus on the most critical regions within DNA-seq datasets.
The proposed model consists of two main stages: the first predicts binding sites, whose outputs are then utilized in the second stage to predict the associated transcription factors. The results demonstrated high predictive performance, achieving an overall accuracy of approximately 99%, reflecting the model’s effectiveness in supporting AI-based genetic data analysis applications.
This scientific achievement highlights the College of Information Technology at the University of Babylon’s commitment to promoting rigorous research and fostering integration between artificial intelligence techniques and the life sciences, contributing to keeping pace with global advancements and expanding the horizons of academic research and innovation.
Research Link : https://link.springer.com/chapter/10.1007/978-3-032-07244-3_15