![]() ![]() ConclusionsĬME provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.ĭescribing the compositions of physical systems, such as in mixtures of industrial chemical reactions, across bacteria in the microbiome, or relative influences in cancer networks is of significant practical importance. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer. First, we measure the relative abundances of different bacteria and infer how they interact. By integrating the prior geometric structure of compositions with sample-specific information, CME infers the underlying multivariate relationships between the constituent components. To resolve both of these issues, we provide a general and data-driven modeling tool for compositional systems called Compositional Maximum Entropy (CME). Secondly, the data lie on a simplex which influences their correlations. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Such a study, however, is challenging for two key reasons. ![]() Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Compositional systems, represented as parts of some whole, are ubiquitous. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |