Zhou, J.; Zhu, K.; Huang, X.; Yuan, J.; Li, Y.

Liquid biopsy, through the analysis of plasma cell-free DNA (cfDNA), is transforming diagnostic medicine by enabling non-invasive insights into the genomic and epigenomic landscape of diseases, most notably cancer. As cfDNA reflects dynamic contributions from multiple tissues, it holds immense potential for early detection, disease monitoring, and personalized treatment. However, the rapid expansion of cfDNA applications has outpaced the availability of unified, versatile computational tools for the systematic analysis of cfDNA data. To address this unmet need, we present cfDNAanalyzer, a comprehensive, user-friendly toolkit that streamlines the feature extraction, selection, and construction of machine learning-based models for disease detection using cfDNA. Designed for flexibility and scalability, cfDNAanalyzer supports multimodal feature extraction, integration, and interpretable outputs, enabling both exploratory research and translational applications. By standardizing and accelerating cfDNA data analysis, cfDNAanalyzer provides a critical resource to advance precision diagnostics and deepen biological insight into disease processes.