Enhancing Genomic Variant Prioritization Using Deep Learning Approaches: A Case Study of Ahmadu Bello University, Zaria, Kaduna State

Enhancing Genomic Variant Prioritization Using Deep Learning Approaches: A Case Study of Ahmadu Bello University, Zaria, Kaduna State

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Background of the Study :
Genomic variant prioritization is a critical step in identifying clinically significant mutations that may drive disease processes. With the rapid accumulation of genomic data, traditional methods of variant analysis are becoming increasingly inefficient. Recent advancements in deep learning have shown promise in automating and enhancing the prioritization of genetic variants by learning complex patterns in high-dimensional data. This study focuses on enhancing genomic variant prioritization using deep learning approaches, with a specific case study conducted at Ahmadu Bello University, Zaria, Kaduna State. The proposed framework will integrate convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze and rank genetic variants based on their predicted pathogenicity and clinical relevance (Aminu, 2023). The study will involve extensive preprocessing of raw sequencing data, followed by the application of feature extraction techniques to capture both local and global genomic patterns. By comparing the performance of deep learning models with conventional variant prioritization methods, the research aims to demonstrate improvements in accuracy, sensitivity, and processing speed (Ibrahim, 2024). Furthermore, the study will incorporate transfer learning to leverage pre-trained models, thereby reducing the need for extensive local training data and improving model generalizability. The integration of these advanced computational techniques is expected to address current limitations in variant interpretation, such as high false-positive rates and the difficulty of discerning functional impacts of rare variants. Additionally, the framework will include visualization tools to facilitate the interpretation of deep learning outputs by clinicians and researchers. Overall, the research seeks to create a robust, scalable, and interpretable deep learning system for genomic variant prioritization that can be readily adopted in clinical settings, ultimately contributing to the advancement of personalized medicine and improved patient outcomes (Bello, 2025).

Statement of the Problem :
The prioritization of genomic variants for clinical interpretation poses significant challenges due to the sheer volume of data generated by modern sequencing technologies. Traditional computational methods often fail to accurately distinguish between pathogenic and benign variants, leading to high false-positive rates and potentially misguiding clinical decisions. In particular, rare and complex variants may be overlooked or misclassified due to the limitations of conventional algorithms (Olawale, 2023). Moreover, the rapid expansion of genomic databases has created a bottleneck in data processing, resulting in delayed diagnoses and reduced efficiency in personalized medicine. Deep learning approaches offer a promising solution; however, their application in variant prioritization is still in its early stages, and many existing models lack sufficient accuracy and interpretability. There is a pressing need to develop a deep learning framework that can effectively prioritize variants by learning intricate patterns in genomic data. This study seeks to address these issues by optimizing deep learning architectures for variant prioritization using data from Ahmadu Bello University. The research will focus on improving model performance through advanced techniques such as transfer learning and ensemble modeling. Additionally, it will tackle the challenge of model interpretability by incorporating visualization methods that highlight the key features driving the predictions. By validating the framework with a curated set of clinically annotated variants, the study aims to demonstrate its potential to streamline the diagnostic process and facilitate targeted therapeutic interventions. Addressing these challenges is essential for translating genomic data into actionable clinical insights and advancing the field of precision medicine (Ibrahim, 2025).

Objectives of the Study:

• To develop and optimize a deep learning framework for genomic variant prioritization.

• To compare the performance of deep learning models with traditional variant prioritization methods.

• To enhance model interpretability and validate the framework using clinically annotated data.

Research Questions:

• How effective are deep learning approaches in prioritizing genomic variants compared to conventional methods?

• What deep learning architectures yield the highest accuracy in variant classification?

• How can model interpretability be improved to facilitate clinical decision-making?

Significance of the Study :
This study is significant as it leverages deep learning to enhance the prioritization of genomic variants, a critical step in clinical diagnostics. The improved accuracy and interpretability of the model will support personalized medicine by enabling more precise identification of disease-causing mutations. The research contributes to advancing computational genomics and offers practical solutions for efficient data analysis in clinical settings, particularly in resource-constrained environments (Aminu, 2023).

Scope and Limitations of the Study:
The study is limited to the development and evaluation of a deep learning framework for genomic variant prioritization using data from Ahmadu Bello University, Zaria, Kaduna State. It does not extend to clinical trials or long-term patient outcome studies.

Definitions of Terms:

1. Deep Learning: A subset of machine learning involving neural networks with many layers used to model complex patterns.

2. Variant Prioritization: The process of ranking genetic variants based on their potential clinical impact.

3. Transfer Learning: A technique in deep learning where a model developed for one task is reused as the starting point for a related task.

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