The article "Genetic Network Inference: From Co-expression Clustering to Reverse Engineering" by Patrik D'haeseleer, Shoudan Liang, and Roland Somogyi reviews the methods and approaches used to understand and model genetic networks. The authors highlight the importance of high-throughput technologies in measuring gene expression and the need to interpret these data to gain insights into biological systems. They discuss clustering methods for co-expression profiles, which help identify shared regulatory inputs and functional pathways. The article also explores advanced analysis techniques to infer causal connections between genes, including discrete Boolean networks and continuous models. The authors emphasize the importance of combining predictive modeling with systematic experimental verification to deepen our understanding of living organisms and for therapeutic targeting and bioengineering. The review covers various aspects of clustering, such as distance measures and algorithms, and discusses different modeling methodologies, including Boolean networks and continuous models. The article concludes by addressing the challenges and future directions in genetic network inference, emphasizing the need for robust methods to handle large-scale data and complex biological networks.The article "Genetic Network Inference: From Co-expression Clustering to Reverse Engineering" by Patrik D'haeseleer, Shoudan Liang, and Roland Somogyi reviews the methods and approaches used to understand and model genetic networks. The authors highlight the importance of high-throughput technologies in measuring gene expression and the need to interpret these data to gain insights into biological systems. They discuss clustering methods for co-expression profiles, which help identify shared regulatory inputs and functional pathways. The article also explores advanced analysis techniques to infer causal connections between genes, including discrete Boolean networks and continuous models. The authors emphasize the importance of combining predictive modeling with systematic experimental verification to deepen our understanding of living organisms and for therapeutic targeting and bioengineering. The review covers various aspects of clustering, such as distance measures and algorithms, and discusses different modeling methodologies, including Boolean networks and continuous models. The article concludes by addressing the challenges and future directions in genetic network inference, emphasizing the need for robust methods to handle large-scale data and complex biological networks.