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Introduction

In recent years, drug repurposing has gained significant attention as a cost-effective and efficient approach for identifying new therapeutic uses for existing drugs. With the ever-increasing volume of biological and chemical data, there is an urgent need for advanced computational methods to facilitate drug repurposing. Cognitive computing, a branch of artificial intelligence that aims to simulate human thought processes, has the potential to revolutionize the drug discovery process by leveraging big data analytics and machine learning algorithms.

This thesis focuses on exploring the application of cognitive computing for drug repurposing, with the aim of identifying novel therapeutic opportunities for existing drugs. By harnessing the power of cognitive computing, researchers can efficiently analyze vast amounts of biological and chemical data to uncover potential drug candidates for specific diseases. The integration of cognitive computing in drug repurposing holds promise for accelerating the drug discovery process and reducing the time and cost associated with traditional drug development approaches.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of drug repurposing
2.2 Role of computational methods in drug repurposing
2.3 Cognitive computing in drug discovery
2.4 Application of artificial intelligence in drug repurposing
2.5 Big data analytics in drug repurposing
2.6 Machine learning techniques for drug repurposing
2.7 Challenges and limitations in drug repurposing
2.8 Recent advancements in drug repurposing
2.9 Opportunities for cognitive computing in drug repurposing
2.10 Future directions in drug repurposing research

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Data integration and feature selection
3.3 Cognitive computing algorithms for drug repurposing
3.4 Model development and validation
3.5 Performance evaluation metrics
3.6 Implementation of cognitive computing framework
3.7 Integration with drug repurposing databases
3.8 Ethical considerations in drug repurposing research

Chapter 4: System Implementation
4.1 Software and hardware requirements
4.2 Data management and storage
4.3 System architecture design
4.4 User interface development
4.5 Testing and evaluation
4.6 Optimization and scalability
4.7 Deployment and maintenance
4.8 Performance tuning

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Future research directions
5.4 Conclusion

Thesis Overview

The field of drug repurposing offers a valuable opportunity to expedite the discovery of new treatments for various diseases by leveraging existing drugs with known safety profiles. However, the traditional drug discovery process is time-consuming, expensive, and often unsuccessful. Cognitive computing, a cutting-edge technology that mimics human thought processes, has emerged as a promising tool for accelerating drug repurposing efforts.

This thesis aims to investigate the application of cognitive computing in drug repurposing, with the goal of identifying new therapeutic uses for existing drugs. By integrating cognitive computing algorithms and big data analytics into the drug discovery process, researchers can efficiently analyze vast amounts of biological and chemical data to uncover novel drug candidates. Through the development of a cognitive computing framework for drug repurposing, this research seeks to demonstrate the potential of this technology to revolutionize the field of drug discovery.

The literature review will provide a comprehensive overview of drug repurposing, the role of computational methods, and recent advancements in the field. The system design and methodology chapter will outline the data collection, integration, and analysis processes, as well as the development and validation of cognitive computing models. The system implementation chapter will detail the software and hardware requirements, system architecture design, and testing procedures. Finally, the conclusion and summary chapter will present the key findings, contributions to the field, and future research directions.

Overall, this thesis aims to contribute to the growing body of knowledge on cognitive computing for drug repurposing and provide valuable insights for researchers and practitioners in the field of drug discovery.

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