Prediction of MHC class II binders using the ant colony search strategy

Karpenko, Oleksiy; Shi, Jianming; Dai, Yang

doi:10.1016/j.artmed.2005.02.002

Cited by 63 publications

(54 citation statements)

References 24 publications

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“…The single most selective event defining T cell epitopes is the binding of peptide fragments to the HLA complexes (Yewdell and Bennink, 1999;Haque and Blum, 2005). Most HLA class II binding prediction methods have been trained and evaluated on very limited data sets covering only a single or a few different HLA class II alleles (Karpenko et al, 2005;Murugan and Dai, 2005;Chang et al, 2006;Salomon and Flower, 2006;Bui et al, 2005;Nielsen et al, 2004;Wan et al, 2006;Brusic et al, 1998). To the best of our knowledge, methods like Propred (Singh and Raghava, 2001) and TEPITOPE (Sturniolo et al, 1999), are experimentally derived virtual matrix-based prediction methods that cover different HLA-DR alleles.…”

Section: Majormentioning

confidence: 99%

Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction

Bhowmick

Saha

Mazzocco

et al. 2014

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Section: Majormentioning

confidence: 99%

Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction

Bhowmick

Saha

Mazzocco

et al. 2014

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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“…Simon et al indicated that it is only through the appropriate selection of parameters that it will be possible for the ACS to show a high level of convergent behavior [38]. Karpenko et al maintained that the algorithm performance is sensitive to the parameter selection in the ACS [39]. Shi et al indicated that the α , ρ, Q, and β parameters in the ACS affect the calculation efficiency and the convergence of the algorithm directly or indirectly [40].…”

Section: Related Workmentioning

confidence: 99%

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

Peker¹,

Sen²,

Kumru³

2013

Turk J Elec Eng & Comp Sci

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Abstract:Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and parameter optimization was taken from the Taguchi method. The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken.In addition to these, a variance analysis was undertaken in order to identify the effect values of the parameters on the system. Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

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“…Recent applications of ACO to problems arising in these areas include the applications to protein folding [83,84], to multiple sequence alignment [76], and to the prediction of major histocompatibility complex (MHC) class II binders [57]. The protein folding problem is one of the most challenging problems in computational biology, molecular biology, biochemistry and physics.…”

Section: Applications Of Aco Algorithms To Discrete Optimization Probmentioning

confidence: 99%