A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to identify transcription factor binding sites

Karabulut, Mustafa; İbrikçi, Turgay

doi:10.1016/j.asoc.2012.04.006

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A PSO algorithm based rule extractor was also proposed for improving the detection accuracy. [339] proposed a PSO-based motif-finding method that utilized a proven Bayesian scoring scheme as the fitness function to identify transcription factor binding sites, which was a vital task in contemporary biology, since it helped researchers to comprehend the regulatory mechanism of gene expression. Liu et al [340] established a classifier based on the two-layer PSO (TLPSO) algorithm and uncertain training sample sets.…”

Section: Medical Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

688

380

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

show abstract

Section: Medical Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

688

380

View full text Add to dashboard Cite

show abstract

“…Self-organization in birds, fish, insects, and herds is a natural behavior for searching for food, hunting, etc. Application of PSO is-renewable energy area [50], automation and control [51], medical [52] and biological engineering [53], operation research [54], and so forth. In a number of studies, the PSO algorithm has been shown to be a successful optimization technique.…”

Section: B Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

et al. 2023

View full text Add to dashboard Cite

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.INDEX TERMS Active magnetic bearing, genetic algorithm, cuckoo search algorithm, particle swarm optimization.

show abstract

“…The key challenge in biomedical problems is located in the huge amount of their data; therefore, problems require approximate algorithms rather than exact algorithms. In [150], Poli et al surveyed more than 25 different biomedical problems that have been solved using PSO, such as gene selection and cancer classification [151], cancer survival prediction [152], protein structure prediction in the 3D HP model [153], identify transcription factor binding sites [154], drug design [155]. In addition, they have been implemented in communication theory, such as antenna selection in multiple-input-multiple-output (MIMO) system [156], optimizing coverage in indoor Ultra-wideband (UWB) communication system [157], scheduling multichannel and multi-timeslot in time constrained wireless sensor networks [158], and non-linear channel equalization [159].…”

Section: Advantages Limitations and Applications Of Si Algorithmsmentioning

confidence: 99%

Review on Nature-Inspired Algorithms

Korani

Mouhoub

2021

SN Oper. Res. Forum

View full text Add to dashboard Cite

A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to identify transcription factor binding sites

Cited by 8 publications

References 21 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Review on Nature-Inspired Algorithms

Contact Info

Product

Resources

About