Sentence examples for modeling the sequence from inspiring English sources

Maximum likelihood methods estimate phylogenetic relations by modeling the sequence evolution (i.e., nucleotide substitution patterns) of genes to construct the gene trees (Swofford et al. 1996).

Schemes for modeling the sequence of minimotifs are well established and have been adopted from previous work modeling protein domains[ 11, 12].

Accurately modeling the sequence substitution process is required for the correct estimation of evolutionary parameters, be they phylogenetic relationships, substitution rates or ancestral states; it is also crucial to simulate realistic data sets.

Finally, since each sequence could potentially fold to any of numerous similar structures, it may be necessary to explicitly consider these 'competing' structures when modeling the sequence probabilities of a given structure (Biswas et al., 2005).

Although the graph G of Y may have a general structure for the problem of modeling the sequence the most simple and important structure is the linear chain structure.

The suggestion of the lognormal standard risk profile is based on the following modeling: The sequence of rare adverse events in time is mathematically modeled as a homogeneous Poisson process.

For homology modeling the sequences of xtGadd45a (NCBI AN CAJ82672) and human SPB2 (NCBI AN NP_076982) were used.

However, as noted by Zhang et al. [ 9], the control sample is critical for modeling the sequencing and mapping biases.

A major drawback of modeling the sequencing error rate using a Poisson distribution is the assumption that the error rate remains conserved across the different sequence regions.

A hidden Markov model is used to model the sequence of observations of individual modalities.

So no Borel normal sequence, and hence no random sequence, can model the sequence of outcomes of a Markov chain, even though each outcome happens by chance.