A Hands-On Introduction to Data Analytics for Biomedical Research

Abstract Artificial intelligence (AI) applications are having increasing impacts in the biomedical sciences. Modern AI tools enable uncovering hidden patterns in large datasets, forecasting outcomes, and numerous other applications. Despite the availability and power of these tools, the rapid expansion and complexity of AI applications can be daunting, and there is a conspicuous absence of consensus on their ethical and responsible use. Misapplication of AI can result in invalid, unclear, or biased outcomes, exacerbated by the unfamiliarity of many biomedical researchers with the underlying mathematical and computational principles. To address these challenges, this review and tutorial paper aims to achieve three primary objectives: (1) highlight prevalent data science applications in biomedical research, including data visualization, dimensionality reduction, missing data imputation, and predictive model training and evaluation; (2) provide comprehensible explanations of the mathematical foundations underpinning these methodologies; and (3) guide readers on the effective use and interpretation of software tools for implementing these methods in biomedical contexts. While introductory, this guide covers core principles essential for understanding advanced applications, empowering readers to critically interpret results, assess tools, and explore the potential and limitations of machine learning in their research. Ultimately, this paper serves as a practical foundation for biomedical researchers to confidently navigate the growing intersection of AI and biomedicine.