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Integrative systems biology approaches build, evaluate, and combine data from thousands of diverse experiments. These strategies rely on methods that effectively identify and summarize gene-gene relationships within individual experiments. For gene-expression datasets, the Pearson correlation is often applied to build coexpression networks because it is both easily interpretable and quick to calculate. Here we develop and evaluate weighted Pearson correlation approaches that better summarize gene expression data into coexpression networks for synchronized cell cycle time-course experiments. These methods use experimental measurements of cell cycle synchrony to estimate appropriate weights through either sliding window or linear regression approaches. We show that these weights improve our ability to build coexpression networks capable of identifying phase-specific functional relationships between genes. We evaluate our method on diverse experiments and find that both weighted strategies outperform the traditional method. This weighted correlation approach is implemented in the Sleipnir library, an open source library used for integrative systems biology. Integrative approaches using properly weighted time-course experiments will provide a more detailed understanding of the processes studied in such experiments.