Topological Data Analysis (TDA) is a mathematical framework that uses concepts from topology to analyze and interpret complex data sets by identifying their underlying shape and structure. This approach allows researchers to uncover patterns, relationships, and anomalies within high-dimensional data, which can be especially useful in understanding disease mechanisms through network-based methods. By transforming data into topological features, TDA provides insights that traditional statistical methods may overlook, making it a powerful tool in systems biology.
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