Ashraf Yaseen

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We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain(More)
In this paper, we present an efficient method implemented on Graphics Processing Unit (GPU), DEMCMC-GPU, for multi-objective continuous optimization problems. The DEMCMC-GPU kernel is the DEMCMC algorithm, which combines the attractive features of Differential Evolution (DE) and Markov Chain Monte Carlo (MCMC) to evolve a population of Markov chains toward(More)
Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models. In this work, we investigate a template-based(More)
Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins. In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of(More)
Evaluating the energy of a protein molecule is one of the most computationally costly operations in many protein structure modeling applications. In this paper, we present an efficient implementation of knowledge-based energy functions by taking advantage of the recent Graphics Processing Unit (GPU) architectures. We use DFIRE, a knowledge-based all-atom(More)
Three-body effects play an important role for obtaining quantitatively high accuracy in a variety of molecular simulation applications. However, evaluation of three-body potentials is computationally costly, generally of O(N3) where N is the number of particles in a system. In this paper, we present a loadbalancing workload distribution scheme for(More)
This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty(More)
Ontology-based reasoning systems have a native rule base but allow also for the addition of application domain-specific rules. Previous work, comparing the performance of these systems, mainly considered performance with supported rule bases. In this paper we present an evaluation of Oracle as an ontology reasoning system with respect to domain-specific(More)
Accurately predicting protein disulfide bonds from sequences is important for modeling the structural and functional characteristics of many proteins. In this paper, we introduce a new approach to enhance disulfide bonding prediction accuracy. We firstly generate the first-order and second-order mean-force potentials according to the amino acid environment(More)
Solvent-accessible surface areas of residues in proteins are key factors in protein folding. Predicting solvent accessibility from protein sequences is significant for modeling the structural and functional characteristics of many proteins. In this work, we introduce an approach of enhancing solvent accessibility prediction accuracy. We derive(More)