Analysis of High-Dimensional and Complex Data, such as Genomic Data, Neuroimaging Data, and Text Data, Using Machine Learning and Dimension Reduction Techniques in Pakistan

Purpose: The aim of the study was to investigate analysis of high-dimensional and complex data, such as genomic data, neuroimaging data, and text data, using machine learning and dimension reduction techniques Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In Pakistan, machine learning and dimension reduction techniques have been applied to analyze high-dimensional and complex data, including genomics, neuroimaging, and text data. These efforts have led to significant advancements in disease genetics, brain imaging, and text mining. While promising, challenges such as data quality and interpretability persist, underscoring the importance of continued research and collaboration in these fields. Unique Contribution to Theory, Practice and Policy: Social network theory, Graph theory & Complex systems theory may be used to anchor future studies on analysis of high-dimensional and complex data, such as genomic data, neuroimaging data, and text data, using machine learning and dimension reduction techniques. Apply machine learning and dimension reduction techniques to genomic data to advance the field of precision medicine. Formulate policies and regulations that address privacy and ethical concerns when dealing with sensitive data, such as genomic information and personal text data