Sciweavers
Explore
Publications
Books
Software
Tutorials
Presentations
Lectures Notes
Datasets
Labs
Conferences
Community
Upcoming
Conferences
Top Ranked Papers
Most Viewed Conferences
Conferences by Acronym
Conferences by Subject
Conferences by Year
Tools
Sci2ools
International Keyboard
Graphical Social Symbols
CSS3 Style Generator
OCR
Web Page to Image
Web Page to PDF
Merge PDF
Split PDF
Latex Equation Editor
Extract Images from PDF
Convert JPEG to PS
Convert Latex to Word
Convert Word to PDF
Image Converter
PDF Converter
Community
Sciweavers
About
Terms of Use
Privacy Policy
Cookies
Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools
20
click to vote
BICOB
2009
Springer
favorite
Email
discuss
report
110
views
Computational Biology
»
more
BICOB 2009
»
Detecting Motifs in a Large Data Set: Applying Probabilistic Insights to Motif Finding
14 years 3 months ago
Download
www.cs.uwaterloo.ca
Christina Boucher, Daniel G. Brown
Real-time Traffic
BICOB 2009
|
Computational Biology
|
claim paper
Related Content
»
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm
»
Metamotifs a generative model for building families of nucleotide position weight matrice...
»
Fast motif recognition via application of statistical thresholds
»
Detecting Motifs in System Call Sequences
»
Finding regulatory elements and regulatory motifs a general probabilistic framework
»
Bioinformatic identification of novel putative photoreceptor specific ciselements
»
Evaluating deterministic motif significance measures in protein databases
»
Maximum entropy modeling of short sequence motifs with applications to RNA splicing signal...
»
Suffix Tree Characterization of Maximal Motifs in Biological Sequences
more »
Post Info
More Details (n/a)
Added
25 May 2010
Updated
25 May 2010
Type
Conference
Year
2009
Where
BICOB
Authors
Christina Boucher, Daniel G. Brown
Comments
(0)
Researcher Info
Computational Biology Study Group
Computer Vision