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» Maximum entropy methods for biological sequence modeling
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TCBB
2010
98views more  TCBB 2010»
13 years 3 months ago
VARUN: Discovering Extensible Motifs under Saturation Constraints
Abstract-The discovery of motifs in biosequences is frequently torn between the rigidity of the model on the one hand and the abundance of candidates on the other. In particular, m...
Alberto Apostolico, Matteo Comin, Laxmi Parida
MICCAI
2004
Springer
14 years 10 months ago
Accurate Quantification of Small-Diameter Tubular Structures in Isotropic CT Volume Data Based on Multiscale Line Filter Respons
Abstract. A method fully utilizing multiscale line filter responses is presented to estimate the point spread function (PSF) of a CT scanner and diameters of small tubular structur...
Yoshinobu Sato, Shuji Yamamoto, Shinichi Tamura
ISMB
1994
13 years 10 months ago
Stochastic Motif Extraction Using Hidden Markov Model
In this paper, westudy the application of an ttMM(hidden Markov model) to the problem of representing protein sequencesby a stochastic motif. Astochastic protein motif represents ...
Yukiko Fujiwara, Minoru Asogawa, Akihiko Konagaya
BMCBI
2007
125views more  BMCBI 2007»
13 years 9 months ago
Statistical tests to compare motif count exceptionalities
Background: Finding over- or under-represented motifs in biological sequences is now a common task in genomics. Thanks to p-value calculation for motif counts, exceptional motifs ...
Stéphane Robin, Sophie Schbath, Vincent Van...
BMCBI
2010
123views more  BMCBI 2010»
13 years 4 months ago
Predicting conserved protein motifs with Sub-HMMs
Background: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is th...
Kevin Horan, Christian R. Shelton, Thomas Girke