I came across this paper in a Web of Science alert that may be helpful in writing our grant:
Shojaie, A., & Sedaghat, N. (2017). How Different Are Estimated Genetic Networks of Cancer Subtypes?. In Big and Complex Data Analysis (pp. 159-192). Springer International Publishing.
It's a book chapter in a book that LMU can access electronically, link to library catalog: http://linus.lmu.edu/record=b3188858~S1
Abstract:
Genetic networks provide compact representations of interactions between genes, and offer a systems perspective into biological processes and cellular functions. Many algorithms have been developed to estimate such networks based on steady-state gene expression profiles. However, the estimated networks using different methods are often very different from each other. On the other hand, it is not clear whether differences observed between estimated networks in two different biological conditions are truly meaningful, or due to variability in estimation procedures. In this paper, we aim to answer these questions by conducting a comprehensive empirical study to compare networks obtained from different estimation methods and for different subtypes of cancer. We evaluate various network descriptors to assess complex properties of estimated networks, beyond their local structures, and propose a simple permutation test for comparing estimated networks. The results provide new insight into properties of estimated networks using different reconstruction methods, as well as differences in estimated networks in different biological conditions.
I came across this paper in a Web of Science alert that may be helpful in writing our grant:
Shojaie, A., & Sedaghat, N. (2017). How Different Are Estimated Genetic Networks of Cancer Subtypes?. In Big and Complex Data Analysis (pp. 159-192). Springer International Publishing.
It's a book chapter in a book that LMU can access electronically, link to library catalog: http://linus.lmu.edu/record=b3188858~S1
Abstract:
Genetic networks provide compact representations of interactions between genes, and offer a systems perspective into biological processes and cellular functions. Many algorithms have been developed to estimate such networks based on steady-state gene expression profiles. However, the estimated networks using different methods are often very different from each other. On the other hand, it is not clear whether differences observed between estimated networks in two different biological conditions are truly meaningful, or due to variability in estimation procedures. In this paper, we aim to answer these questions by conducting a comprehensive empirical study to compare networks obtained from different estimation methods and for different subtypes of cancer. We evaluate various network descriptors to assess complex properties of estimated networks, beyond their local structures, and propose a simple permutation test for comparing estimated networks. The results provide new insight into properties of estimated networks using different reconstruction methods, as well as differences in estimated networks in different biological conditions.