Enhancing Computational Biology-Based Pathogen Detection Techniques Using AI: A Case Study of Federal University, Dutsin-Ma, Katsina State
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- NGN 5000
Background of the Study
Pathogen detection is critical for identifying and responding to infectious diseases, particularly in regions where emerging pathogens are a constant threat. Traditional pathogen detection techniques, such as PCR and culturing, have limitations, including high costs, time consumption, and the need for specialized expertise. Computational biology, particularly when combined with artificial intelligence (AI), offers promising alternatives for improving pathogen detection. AI-powered techniques such as machine learning, deep learning, and pattern recognition can enhance the speed, accuracy, and efficiency of pathogen detection by analyzing vast datasets and identifying subtle biological patterns. This study at Federal University, Dutsin-Ma, Katsina State, aims to explore the application of AI in enhancing computational biology-based pathogen detection techniques, which could play a crucial role in managing public health threats in Nigeria.
Statement of the Problem
In many developing countries, including Nigeria, there is a lack of rapid, cost-effective, and accurate diagnostic tools for detecting emerging pathogens. While computational biology methods have shown promise in pathogen detection, their application is often limited by the availability of training data, the complexity of the algorithms, and the need for specialized computational resources. This study seeks to investigate how AI can enhance computational biology methods to improve pathogen detection in Nigerian healthcare settings, where timely and accurate detection is critical for disease prevention.
Objectives of the Study
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To explore the application of AI in enhancing computational biology-based pathogen detection techniques.
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To assess the effectiveness of AI models in identifying pathogens from genomic data.
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To develop an AI-enhanced pathogen detection framework for use at Federal University, Dutsin-Ma, Katsina State.
Research Questions
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How can AI enhance computational biology-based pathogen detection techniques?
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What is the effectiveness of AI in identifying pathogens from genomic data?
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How can AI-powered pathogen detection systems be implemented at Federal University, Dutsin-Ma?
Significance of the Study
This study will contribute to improving pathogen detection systems, offering faster, more accurate diagnostic capabilities. By leveraging AI in computational biology, the study will provide a significant step toward more efficient public health monitoring and response to infectious diseases in Nigeria.
Scope and Limitations of the Study
The study will focus on AI-enhanced pathogen detection techniques in computational biology at Federal University, Dutsin-Ma. Limitations include the availability of comprehensive genomic datasets for pathogen detection and the computational resources required to develop and implement AI models.
Definitions of Terms
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Pathogen Detection: The process of identifying the presence of disease-causing organisms, such as bacteria, viruses, or fungi, through various diagnostic methods.
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Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn from experience.
Computational Biology: The use of computational techniques and models to study and analyze biological systems and data
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