Complex Thinking Abilities in the Rapidly Evolving Field of Genomics and Personalized Medicine: Analysis of Actionability on Cancer with ChatGPT and Literature

Abstract

This study aims to explore complex thinking abilities within the field of genomics and personalized medicine, focusing on the analysis of actionable cancer data using Big Data Analytics (BDA) and ChatGPT. It seeks to understand how these advanced technologies can be harnessed to derive more actionable approaches for students and professionals in genetics, biology, medicine, and related fields. Methods: The research methodology involves a machine learning (ML) analysis to visualize the distribution of genes based on top ten actionability counts, development status, and drug combinations. This includes ChatGPT prompts for visualization of gene distribution and the use of pivot tables for data validation. The study facilitates complex data analysis and decision-making processes in genomics. The findings reveal that BDA and ChatGPT can significantly improve the analysis and interpretation of genomic data. Visualization techniques enabled by these technologies allow for the identification of patterns, correlations, and predictive models. These insights can lead to more accurate diagnoses, personalized treatment plans, and a better understanding of drug combinations and mutations in cancer. This research highlights the essential role of automation and open access in managing and interpreting large volumes of genomic data efficiently. Conclusion: The integration of BDA and ChatGPT into genomics and personalized medicine offers promising avenues for advancing personalized medicine, enhancing clinical decision-making, and fostering research and development in the field of cancer.