The paper "Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges" by Purvesh Khatri, Marina Sirotta, and Atul J. Butte reviews the evolution of pathway analysis methods over the past decade, focusing on three generations of approaches: Over-Representation Analysis (ORA), Functional Class Scoring (FCS), and Pathway Topology (PT)-based methods. ORA methods statistically evaluate the fraction of genes in a pathway that show differential expression, but they have limitations such as ignoring gene-specific changes and assuming independence between genes and pathways. FCS methods address these issues by considering coordinated changes in gene expression within pathways, but they still assume independence among pathways. PT-based methods further improve upon FCS by incorporating pathway topology, which accounts for interactions between genes. However, all methods face challenges such as low-resolution knowledge bases, incomplete and inaccurate annotations, and the inability to model dynamic responses and external stimuli. The authors identify the need for high-resolution annotations, dynamic modeling, and integration of external stimuli to advance pathway analysis. They emphasize the importance of community efforts to address these challenges and develop the next generation of pathway analysis tools that can better utilize high-throughput technologies to understand complex biological systems.The paper "Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges" by Purvesh Khatri, Marina Sirotta, and Atul J. Butte reviews the evolution of pathway analysis methods over the past decade, focusing on three generations of approaches: Over-Representation Analysis (ORA), Functional Class Scoring (FCS), and Pathway Topology (PT)-based methods. ORA methods statistically evaluate the fraction of genes in a pathway that show differential expression, but they have limitations such as ignoring gene-specific changes and assuming independence between genes and pathways. FCS methods address these issues by considering coordinated changes in gene expression within pathways, but they still assume independence among pathways. PT-based methods further improve upon FCS by incorporating pathway topology, which accounts for interactions between genes. However, all methods face challenges such as low-resolution knowledge bases, incomplete and inaccurate annotations, and the inability to model dynamic responses and external stimuli. The authors identify the need for high-resolution annotations, dynamic modeling, and integration of external stimuli to advance pathway analysis. They emphasize the importance of community efforts to address these challenges and develop the next generation of pathway analysis tools that can better utilize high-throughput technologies to understand complex biological systems.