the noise expression can be represented by Y n ( t ) = V n ( t ) cos ( Φ n ( t ) ) cos ( Φ ( t ) ) + V n ( t ) sin ( Φ n ( t ) ) sin ( Φ ( t ) ) A c (22).
Note that every Boolean expression can be represented as a logic tree; see Figure 1.
Alternatively, expression can be represented by reads per kilobase per million mapped reads, RPKM m =θ m ×10/ l m, introduced by Mortazavi et al. (2008).
For example, "change in expression" can be represented as one of the following verb-phrases: Change MicroRNA-21 Expression, Expression of caveolin-1 was changed, Change in high levels of high-mobility group A2 expression, change of the let-7e and miR-23a/b expression, expression of miR-199b-5p miR-199b-5p miR-199b-5pases was sinnificanthe changed, etc.
For example, "change in expression" can be represented in one of the following verb-phrases: Change MicroRNA-21 Expression, Expression of caveolin-1 was changed, Change in high levels of high-mobility group A2 expression, change of the let-7e and miR-23a/b expression, expression of miR-199b-5p miR-199b-5p miR-199b-5p miR-199b-5pninicanthe changed, etc.
Gene expression networks can be represented as graphs with TFs and other expression regulators acting as nodes and functional interactions between regulators and between regulators and their targets as directional edges [ 12, 15].
Gene expression levels can be represented as reads per kilobase per million mapped reads (RPKM) in RNA-Seq [ 39].
Gene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal (switch-like) distributions.
A gene co-expression network can be represented by an adjacency matrix A = [ a ij ], where a ij is the weight of a connection between two nodes i and j.
