An automatic motif recognition algorithm in DNA sequences based on particle swarm optimization and random projection

2017 ◽  
Author(s):  
Hongwei Ge ◽  
Liang Sun ◽  
Yao Yao ◽  
Jinghong Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Random Projection ◽  
Recognition Algorithm ◽  
Swarm Optimization ◽  
Motif Recognition

Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design

2020 ◽  
Vol 15 (12) ◽  
pp. 1450-1459
Author(s):  
Ying Niu ◽  
Hangyu Zhou ◽  
Shida Wang ◽  
Kai Zhao ◽  
Xiaoxiao Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design

The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.

Function Minimization In DNA Sequence Design Based On Binary Particle Swarm Optimization

2012 ◽  
Author(s):  
Zuwairie Ibrahim ◽  
Noor Khafifah Khalid ◽  
Ismail Sheng ◽  
Salinda Buyamin ◽  
Zulkifli Md. Yusof ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Melting Temperature ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Dna Sequence Design

In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an elementary problem and one of the most practical and important research topics. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints. Basically, the fitness of DNA sequences can be evaluated using four objective functions, namely, similarity, Hmeasure, continuity and hairpin. In this paper, binary particle swarm optimization (BinPSO) is proposed to minimize those objective functions individually, subjected to two constraints: melting temperature and GCcontent. An implementation of the optimization process is presented using 20 particles and the results obtained shows the correctness of PSO computation, where the minimized values for each objective can be achieved. Kata kunci: DNA sequence design; binary particle swarm optimization; objective function; constraints; fitness function Dalam DNA berasaskan pengiraan dan nanoteknologi DNA, reka bentuk jujukan–jujukan DNA baik telah menjadi satu masalah asas dan salah satu topik–topik penyelidikan penting dan paling praktikal. Walaupun reka bentuk DNA berjujukan adalah bergantung kepada pada protokol eksperimen biologi, ia amat diperlukan bagi mewujudkan satu kaedah untuk reka bentuk bersistem DNA berjujukan, yang boleh digunakan untuk pelbagai reka bentuk kekangan. Pada asasnya, kecergasan DNA berjujukan boleh dinilaikan menggunakan empat fungsi–fungsi objektif, iaitu, similarity, Hmeasure, continuity dan hairpin. Dalam kertas kerja ini, pengoptimuman kerumunan zarah perduaan (BinPSO) dicadangkan untuk meminimumkan fungsi–fungsi objektif tersebut secara individu, tertakluk kepada dua kekangan: melting temperature dan GCcontent Satu perlaksanaan proses pengoptimuman dibentangkan menggunakan 20 zarah–zarah dan keputusan yang diperolehi menunjukkan kebenaran pengiraan PSO, di mana nilai–nilai yang minimum untuk setiap objektif dapat dicapai. Key words: Reka bentuk jujukan DNA; pengoptimuman kerumunan zarah perduaan; fungsi objektif; kekangan; fungsi kecergasan

scholarly journals Discovery of DNA Motif Utilising an Integrated Strategy Based on Random Projection and Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Hongwei Ge ◽  
Jinghong Yu ◽  
Liang Sun ◽  
Zhen Wang ◽  
Yao Yao
Keyword(s):  
Transcription Factor ◽  
Particle Swarm Optimization ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Random Projection ◽  
Transcription Factor Binding ◽  
Swarm Optimization ◽  
Factor Binding ◽  
Objective Space

During the process of gene expression and regulation, the DNA genetic information can be transferred to protein by means of transcription. The recognition of transcription factor binding sites can help to understand the evolutionary relations among different sequences. Thus, the problem of recognition of transcription factor binding sites, i.e., motif recognition, plays an important role for understanding the biological functions or meanings of sequences. However, when the established search space processes much noise subsequences, many optimization algorithms tend to be trapped into local optimum. In order to solve this problem, a particle swarm optimization and random projection-based algorithm (PSORPS) is proposed for recognizing DNA motifs. First, a random projection strategy is employed to filter the noise subsequences for constructing the objective space. Moreover, the sequence segments distributed in the majority of DNA sequences can be obtained and used for the population initialization of PSO. Then, the motifs of DNA sequences can be automatically searched by using a designed PSO algorithm in the constructed l-mer objective space. Finally, to alleviate the base deviation and further improve the recognition accuracy, the two operators of associated drift and independent drift are performed on the optimization results obtained by PSO. The experiments are conducted on real-world biological datasets, and the experimental results verify the effectiveness of the proposed algorithm.

A Model to Optimize DNA Sequences Based on Particle Swarm Optimization

2008 ◽  
Author(s):  
Noor Khafifah Khalid ◽  
Tri Basuki Kurniawan ◽  
Zuwairie Ibrahim ◽  
Zulkifli Md Yusof ◽  
Marzuki Khalid ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Swarm Optimization

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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