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Let (mathbb {O}=left {O_{r},y_{r}right }_{r=1}^{R}) be a set of R labeled sequences of length T where (O_{r}=left {O_{r}^{(1)},cdots,O_{r}^{(T }right }) and y r ∈{1,⋯,C} is the label (class) of sequence O r. First, we need to identify subgroups of observations that have common patterns.

Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names.

By comparison of the labeled sequence length to the domain size, compaction ratios were estimated to 1 129 (p53), 1 235 (p58), and 1 396 (c-myc).

The observation model is built from the training data of manually labeled sequences using a finite set of particles within the feature point centered window.

Furthermore, assume that the amount of target hybridized to a specific probe is proportional to the number of labeled sequences.

In this kind of problems, the training sets generally consist of pairs of observed and label sequences and very often the number of the different labels representing the experimental evidence is small compared to the grammar requirements and the length distribution of the segments for the different labels.

We use a forward backward algorithm to sample the label sequence of a segment from position i to j.

These probabilities are equivalent to the total sum of the probabilities of all possible label sequences of which the central labels are 'Phos', assigned by a CRF given the flanking sequence of amino acids.

In CTF, the possible values for label sequence of one bin is 0 (non-TFBS) and 1 (TFBS).

The most common usage of SO is to label sequence annotations with appropriate SO terms.

Each sub-bucket contains the following control information: the size of the bucket label, the sequence of the bucket label (2-bit encoded), the number of elements, m, in the sub-bucket minus one (this is encoded with an 8-bit unsigned integer and thus has a maximum value of 255), and a sequence of m encoded reads.

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