We have formulated a sequence-based approach that enables the identification of TF-specific binding motifs.
Therefore, we have formulated a comprehensive sequence-based comparative approach for the prediction of TF-specific operators in bacteria.
One of the most important but also most difficult problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information.
Actually, one of the most important but also most difficult problems in computational biology is how to effectively formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence-order information.
Many software engineering projects involve a significant design component in which an algorithm must be formulated as a sequence of processing steps that meets a solution criterion.
After discretization the evolutionary state problem, associated with the optimal shape problem, is formulated as a sequence of nonsmooth equations and a general form of the optimal design problem is treated by using a nonsmooth approach.
In this study, the CHEMDNER task is formulated as a sequence labelling problem.
Than the corresponding mixed complimentary problem can be formulated as a sequence of conditions on markets, profits and budget constraints.
Named entity recognition is typically formulated as a sequence labeling problem which can be defined as follows: given a sequence of input tokens x = (x1 … x n ), and a set of labels L, determine a sequence of labels y = (y1, …, y n ) for the tokens such that y i ∈ L for 1 ≤ i ≤ n.
Since μ(t) is analogous to the spike train of each neuron, we need to formulate a binary sequence that shares the correlational structure of a random walk.
To investigate this more rigorously, we first formulated a model of high-throughput sequencing data generation which takes into account three primary influencing factors: (i) underlying fragment generation distribution, (ii) CG-dependent sequencing bias, and (iii) mapping bias (see Equation 3 in Materials and Methods).
