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1 cific scoring matrix (PSSM), also known as a position weight matrix.
2 user-defined TFBSs given as a consensus or a position weight matrix.
3 variable sequences, were used to establish a position weight matrix and a matrix similarity (MS) scor
4                            BML utilizes both position weight matrix and dinucleotide weight matrix, t
5 n, HIGEDA can identify gapped motifs using a position weight matrix and dynamic programming to genera
6 ree (CART) statistical model, which involves position weight matrix and human-mouse sequence similari
7  microarrays to derive a novel SOX4-specific position-weight matrix and determined that SOX4 binding
8 AR template performance based on sequence: a position weight matrix approach with support vector mach
9 cher description of the MEF2 binding site, a position weight matrix, can be reliably constructed and
10 -regulators (including androgen receptor) in Position Weight Matrix, Chip-Seq, and Hi-C experimental
11 itional supplementary annotation data (e.g., position weight matrix matches, conservation scores, str
12 dentification algorithm, which is based on a position weight matrix model that does not require addit
13 factor (TF) is typically represented using a position weight matrix model, which implicitly assumes t
14 moter strength and function, we used modular position weight matrix models comprised of each promoter
15 uding simple mononucleotide and dinucleotide position weight matrix models.
16  hypergeometric-based scoring function and a position-weight matrix optimization routine to identify
17 uch as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and
18 TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a
19 ('words') of length k ('k-mers'), as well as position weight matrix (PWM) and graphical sequence logo
20 ('words') of length k ('k-mers'), as well as position weight matrix (PWM) and graphical sequence logo
21     Also the simple method of constructing a position weight matrix (PWM) by comparing the frequency
22                                          The position weight matrix (PWM) derived for BqsR uncovered
23           In this system, we (i) construct a position weight matrix (PWM) from a collection of experi
24                                          The position weight matrix (PWM) is a popular method to mode
25              Such models largely rely on the position weight matrix (PWM) model for DNA binding, and
26                We adapt the well-established position weight matrix (PWM) model of transcription fact
27 DSMC) motif discovery technique based on the position weight matrix (PWM) model to locate conserved m
28 lly infer the optimal cutoff threshold for a position weight matrix (PWM) of a motif identified from
29  in sine oculis (so) was used to construct a position weight matrix (PWM) of the Ey protein.
30  (TF) binding site analysis using a TRANSFAC position weight matrix (PWM) search, 86% of non-specific
31                                       When a position weight matrix (PWM) was constructed from the pr
32 red target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in
33  of k-mer level data in a rank-dependent and position weight matrix (PWM)-independent manner.
34 dictions based only on matches to a sequence position weight matrix (PWM).
35 perimentally determined and represented in a position weight matrix (PWM).
36 omogeneous partition is then summarized by a position weight matrix (PWM).
37 , fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing a
38                                          The position-weight matrix (PWM) is a useful representation
39 wly identified sites were used to generate a position-weighted matrix (PWM) for EBNA1's DNA-binding s
40 r the quality of the matches of a particular position weight matrix relative to another set.
41 nding site (rs1277068603; frequency=0.00001; position weight matrix score=0.98).
42 t k-mer searches on genomic data, as well as position weight matrix searches.
43  we developed two methods, Kmer-Sum and PWM (Position Weight Matrix) stacking, for full-length bindin
44                             We constructed a position weight matrix using these in vivo data and scan
45 on incorporating p53 binding strength into a position weight matrix, we selected 32 SNPs in putative