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RECOMB
2006
Springer
16 years 6 months ago
Detecting the Dependent Evolution of Biosequences
Abstract. A probabilistic graphical model is developed in order to detect the dependent evolution between different sites in biological sequences. Given a multiple sequence alignme...
Jeremy Darot, Chen-Hsiang Yeang, David Haussler
ICML
2004
IEEE
16 years 7 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
216
Voted
ICASSP
2011
IEEE
14 years 10 months ago
Cooperative Maximum Likelihood estimation for fluid flow dynamics in biosensor arrays
Abstract—This paper deals with estimation of the concentration of target molecules in a fluid when it flows past multiple biosensors. The fluid flow is modelled as an advecti...
Maryam Abolfath Beygi, Vikram Krishnamurthy
CD
2005
Springer
15 years 12 months ago
Modelling Deployment Using Feature Descriptions and State Models for Component-Based Software Product Families
Abstract. Products within a product family are composed of different component configurations where components have different variable features and a large amount of dependency re...
Slinger Jansen, Sjaak Brinkkemper
COMAD
2008
15 years 7 months ago
REBMEC: Repeat Based Maximum Entropy Classifier for Biological Sequences
An important problem in biological data analysis is to predict the family of a newly discovered sequence like a protein or DNA sequence, using the collection of available sequence...
Pratibha Rani, Vikram Pudi