Alain Deschênes

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This paper builds on previous research from an EA used to predict secondary structure of RNA molecules. The IEA predicts which specific canonical base pairs will form hydrogen bonds and helices. Three new thermodynamic models were integrated into our EA. The first is based on a modification to our original base pair model. The last two, INN and INN-HB, add(More)
This work builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA has the goal of predicting which canonical base pairs will form hydrogen bonds and helices. The addition of stacking energies, through INN and INN-HB, to our thermodynamic model has enhanced our predictions. We test three RNA sequences of lengths(More)
This work presents a coarse-grained distributed genetic algorithm (GA) for RNA secondary structure prediction. This research builds on previous work and contains two new thermodynamic models, INN and INN-HB, which add stacking-energies using base pair adjacencies. Comparison tests were performed against the original serial GA on known structures that are(More)
Two extensive analyzes on RnaPredict, an evolutionary algorithm for RNA folding, are presented here. The first study evaluates the performance of individual nearest neighbor (INN) and individual nearest neighbor-hydrogen bond (INN-HB), two stacking-energy thermodynamic models; the criteria for comparison is the correlation between the prediction accuracy(More)
This paper presents a fully parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a(More)
This paper presents a parallel version of <i>RnaPredict</i>, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a(More)
With the expansion in the application of library methods in medicinal chemistry and chemical biology there is a growing need for improved technology for the design of novel templates that are well suited for the synthesis of libraries targeted toward specific subsets of protein families. In this report, we delineate an improved stepwise general method that(More)