Sentence examples for given a set of sequences from inspiring English sources
Given a set of sequences that are putative regulatory sequences (i.e., WPHs, HSNs, LSNs), we evaluated their predictive potential by computing the significance of their overlap with one or more test sets.
Given a set of sequences, the objective is to perform simultaneous inference of tree topology, rates, and times.
Given a set of sequences, it searches for motifs that occur (with a bounded number of mismatches) in as many sequences as possible.
Given a set of sequences that were tested in a high throughput experiment such as ChIP-Seq [ 18], CLIP [ 20] and others, they can be ranked according to the measured binding affinities, yielding a ranked list L1.
Our goal here is to learn the best setting for λ, the weights of features in the CRF model given a set of sequences as training data with their nucleotide types x and state labels y.
The features one is interested in and the way in which these features are described ultimately define the correct alignment, and in theory, given a set of sequences, each feature type may define a distinct optimal alignment.
Given a set of biological sequences, the primary objective of sequence alignment is to predict the best overall mapping between the sequences, which accurately aligns the homologous regions that are embedded in them.
Motif finding algorithms have been widely used in this field for finding sequence signatures when given a set of related sequences (pattern mining).
Given a set of N sequences, FOLDALIGN tries to come up with a subset of sequences which contain the most significant common motif.
Given a set of n sequences and a motif model < l, d>, randomly designate two sequences from the sample as reference sequences, namely R1 and R2, and the rest as non reference sequences S1, S2,..., Sn-2.
In both of these applications the computational problem is the same: given a set of DNA sequences to be classified (henceforth called "objects") and a set of reference sequences (e.g., genus-level sequences, chromosome arms, etc., henceforth called "targets"), identify which target is the most likely origin of each object based on sequence similarity.
