Biotechnology — Predictive computational models in personalized medicine research — Part 1: Constructing, verifying and validating models

This technical specification establishes a structured framework for the development, validation, and verification of predictive computational models within the field of biotechnology, specifically tailored for personalized medicine research. It outlines fundamental principles and methodological requirements to ensure that mathematical algorithms accurately interpret biological data to support individualized healthcare decisions. The document addresses critical aspects such as model architecture design, data quality assessment, and rigorous testing protocols necessary to demonstrate model reliability. It provides guidance on documenting the entire lifecycle of a predictive tool, from initial conception through to deployment and ongoing monitoring. By adhering to these guidelines, practitioners can enhance the robustness of their analytical tools, ensuring they meet high standards of scientific accuracy and reproducibility. The approach emphasizes the integration of statistical methods with biological insights, fostering trust in computational outputs used for clinical applications. This specification serves as a comprehensive reference for researchers and developers aiming to align their modeling efforts with established international best practices in the domain of health informatics and genomic analysis.