We have presented a method for computing connections between biological processes specific to a biological context corresponding to comparing gene expression measurements from two conditions (e.g., case-control gene expression studies).
By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution.
For some structure pairs, considering the whole rigid region in computing the connection cost resulted in significant improvements.
In Figure 4, if we were connecting P5 to R2 that ends with P4, FATCAT would compute the connection cost C(P5, P4) but FlexSnap would compute C((P1, P4) → P5) since P4 belongs to the rigid region that contains P1.
However, PPR computes base connection values and the weight of nodes based on such attributes as the inter-node relationship, the related title of search that connects them, and the current connection types between them.
Sixty MOE-type descriptors can be computed from connection table information based on atomic contributions to Van der Waals surface area, LogP, molar refractivity, partial charge, and E-state value.
We compute estimated connection probabilities p mr between all possible combinations metabolite-reaction as (3) where k m is the degree of the metabolite and μ = 1/ R ensures that network realizations with these connection probabilities have the same number of links as the observed network.
However, instead of using sine functions to couple the oscillators, we use the neuron-to-neuron connections in the neural model to compute intersegmental connections between oscillators.
The second sub-module (Algorithm 2) consists of computing the Galois connection of L i s t S x according to the specified value of δ (i.e., CL Sx ) 3.
