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» Maximum entropy methods for biological sequence modeling
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ICASSP
2010
IEEE
13 years 9 months ago
Language recognition using deep-structured conditional random fields
We present a novel language identification technique using our recently developed deep-structured conditional random fields (CRFs). The deep-structured CRF is a multi-layer CRF mo...
Dong Yu, Shizhen Wang, Zahi Karam, Li Deng
CMSB
2004
Springer
14 years 2 months ago
Combining State-Based and Scenario-Based Approaches in Modeling Biological Systems
Biological systems have recently been shown to share many of the properties of reactive systems. This observation has led to the idea of using methods devised for the construction ...
Jasmin Fisher, David Harel, E. Jane Albert Hubbard...
BMCBI
2008
128views more  BMCBI 2008»
13 years 9 months ago
Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor bindin
Background: Biologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (T...
Nak-Kyeong Kim, Kannan Tharakaraman, Leonardo Mari...
IDEAL
2000
Springer
14 years 25 days ago
Quantization of Continuous Input Variables for Binary Classification
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
Michal Skubacz, Jaakko Hollmén
RECOMB
2005
Springer
14 years 9 months ago
Likely Scenarios of Intron Evolution
Whether common ancestors of eukaryotes and prokaryotes had introns is one of the oldest unanswered questions in molecular evolution. Recently completed genome sequences have been u...
Miklós Csürös