We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
Background: Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identifica...
Harald Barsnes, Svein-Ole Mikalsen, Ingvar Eidhamm...
This paper presents an open contour tracking method that employs an arc-emission Hidden Markov Model (HMM). The algorithm encodes the shape information of the structure in a spati...
Mehmet Emre Sargin, Alphan Altinok, Kenneth Rose, ...
Background: Protein sequence alignments have become indispensable for virtually any evolutionary, structural or functional study involving proteins. Modern sequence search and com...
?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contribut...