Evaluation of AI-Based Approaches for Genomic Variant Interpretation in Cancer: A Case Study of Kaduna State University, Kaduna State
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- NGN 5000
Background of the Study
Cancer remains one of the leading causes of death worldwide, with its complex and multifactorial nature making it challenging to diagnose, treat, and predict. Over the years, significant advancements have been made in understanding the genetic basis of cancer, which has led to the identification of specific genomic variants that drive cancer development. However, interpreting these variants in a clinically meaningful way remains a significant challenge. The traditional methods of interpreting genomic variants are time-consuming, labor-intensive, and often insufficient for providing accurate predictions about cancer progression and treatment response. Artificial intelligence (AI), particularly machine learning algorithms, has emerged as a promising solution for enhancing genomic variant interpretation by automating the analysis process and providing more accurate and reliable predictions (Lee et al., 2024).
AI-based approaches for genomic variant interpretation rely on algorithms that can process large-scale genomic data, identify potential mutations, and correlate them with known cancer-related pathways. These tools have the potential to revolutionize the way genomic data is analyzed, providing quicker and more reliable results that can aid in cancer diagnosis, prognosis, and treatment decisions. Despite the advancements in AI-based approaches, their integration into clinical practice remains limited, especially in developing countries like Nigeria, where access to genomic technologies and trained bioinformaticians is often scarce (Ahmed et al., 2023). Furthermore, the ability of AI systems to interpret variants across diverse ethnic populations, with their unique genomic characteristics, has not been adequately explored.
This study at Kaduna State University will evaluate the use of AI-based approaches in interpreting genomic variants related to cancer. By focusing on the potential of AI to improve the accuracy and efficiency of variant interpretation, the research aims to develop a framework for integrating these approaches into cancer care in Nigeria, providing insights into both the opportunities and challenges faced by healthcare providers in resource-limited settings.
Statement of the Problem
Cancer diagnosis and treatment in Nigeria are hindered by the limited availability of advanced genomic technologies and expertise. While AI has the potential to improve genomic variant interpretation, it has not been sufficiently explored in the Nigerian healthcare context. The lack of standardized methods for interpreting genomic variants in cancer leads to delays in diagnosis, misdiagnosis, and inappropriate treatment. This study seeks to evaluate the effectiveness of AI-based approaches for genomic variant interpretation in cancer, with a focus on addressing the challenges faced by Nigerian researchers and healthcare professionals.
Objectives of the Study
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To evaluate the effectiveness of AI-based approaches in interpreting genomic variants related to cancer in Nigerian populations.
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To identify key challenges in applying AI-based genomic variant interpretation in the Nigerian healthcare system.
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To propose strategies for integrating AI-based tools into cancer diagnostic and treatment workflows in Nigeria.
Research Questions
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How effective are AI-based approaches in interpreting genomic variants related to cancer in Nigerian populations?
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What challenges exist in applying AI-based genomic variant interpretation methods in Nigeria?
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How can AI-based tools be integrated into the cancer diagnostic and treatment processes in Nigeria?
Significance of the Study
This study will contribute to improving cancer care in Nigeria by evaluating the potential of AI-based genomic variant interpretation tools. The findings will help streamline cancer diagnosis and treatment, making the process faster and more accurate. Additionally, the study will provide valuable insights into the feasibility and challenges of implementing AI-based approaches in the Nigerian healthcare system, paving the way for more widespread adoption of these technologies.
Scope and Limitations of the Study
The study will focus on evaluating AI-based approaches for interpreting genomic variants in the context of cancer. It will assess the effectiveness of these tools in the Nigerian healthcare setting but will not explore the clinical outcomes of AI-based interpretation. The research is limited to cancer genomic variant interpretation and does not address other aspects of cancer care.
Definitions of Terms
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Artificial Intelligence (AI): The use of algorithms and machine learning models to simulate human intelligence and perform tasks that typically require human intervention.
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Genomic Variant Interpretation: The process of analyzing genetic mutations or variations to determine their potential impact on disease development and progression.
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Cancer: A group of diseases characterized by uncontrolled cell growth and spread to other parts of the body.
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