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Network inference in genomics
Last modified: 2017-06-20
Abstract
The whole concept of network inference in genomics has multiple
meanings and interpretations. It can refer to “causal†or “topological†considerations, i.e., learning about functional relationships in the genomic system or to considerations about the structure of the overall genomic network. Moreover, the genomic network does not really exists and can refer to gene regulatory networks, cell signalling networks, metabolic networks etc.
In this manuscript I aim to clarify the underlying genomics in order to motivate a hierarchy of four network inference strategies, starting at the single cell level and finishing at global structural network inference. It will involve stochastic and ordinary differential equation models, causal inference and graphical modelling.
meanings and interpretations. It can refer to “causal†or “topological†considerations, i.e., learning about functional relationships in the genomic system or to considerations about the structure of the overall genomic network. Moreover, the genomic network does not really exists and can refer to gene regulatory networks, cell signalling networks, metabolic networks etc.
In this manuscript I aim to clarify the underlying genomics in order to motivate a hierarchy of four network inference strategies, starting at the single cell level and finishing at global structural network inference. It will involve stochastic and ordinary differential equation models, causal inference and graphical modelling.