We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local wi...
Scott C. Schmidler, Jun S. Liu, Douglas L. Brutlag
Background: RNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs). Such methods can readily co...
The 2-interval pattern problem over its various models and restrictions was proposed by Vialette (2004) for the application of RNA secondary structure prediction. We present an O(n...
Background: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve ...
Scott Montgomerie, Shan Sundararaj, Warren J. Gall...
Background: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algor...
Background: The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested t...
Amir Momen-Roknabadi, Mehdi Sadeghi, Hamid Pezeshk...
Background: To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of...
Motivation: Existing methods for protein sequence analysis are generally firstorder and inherently assume that each position is independent. We develop a general framework for int...
Martin Madera, Ryan Calmus, Grant Thiltgen, Kevin ...
Protein secondary structure prediction is an important step towards understanding the relation between protein sequence and structure. However, most current prediction methods use...
Yan Liu, Jaime G. Carbonell, Judith Klein-Seethara...
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We a...