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Exact(12)
Overall, our method has the superiority like other exact optimal motif finding methods to find the optimal motif in the absence of statistical overrepresentation and yet without sacrificing prediction accuracy.
Furthermore, we have compared the prediction accuracy of our method with the statistical motif finding methods mentioned above and find that our method is equal to and sometimes better than these methods.
We have created fast motif finding methods MOST and MOST+.
Table 4 shows the averaged measurements of iTriplet together with four other motif finding methods.
Assessment on site-level accuracy of motif finding methods was conducted by using the pipeline described in Methods section.
Practically all motif finding methods use some form of supervised learning (training from known data) [ 59- 61].
Similar(48)
To our knowledge, iTriplet is the most parallelizable motif finding method in the family of guaranteed optimal motif finding algorithms developed so far.
Simonis et al. [ 23] combined a string-based motif finding method and linear discriminant analysis to identify motif combinations that can separate true regulons from false ones.
iTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods.
A straightforward method is to identity a set of differentially expressed or co-expressed genes, and then search their promoter sequences for known transcription factor binding sites or use de nova motif finding method to identify enriched motifs [ 21].
We have combined protein motif search and gene finding methods to identify genes encoding proteins containing specific domains.
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