A comprehensive survey on genetic algorithms for DNA motif prediction

Lee, Nung Kion; Li, Xi; Wang, Dianhui

doi:10.1016/j.ins.2018.07.004

Cited by 27 publications

(13 citation statements)

References 71 publications

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“…In order to carry out a comprehensive testing, three groups of simulated datasets are generated by controlling values of t, n, l, d, q and g. The first group of simulated datasets corresponds to the data with different (l, d) motifs obtained by fixing t = 3000, n = 200, q = 0.5 and g = 0.5 and varying (l, d) from (9, 2) to (21,8). The second group of simulated datasets corresponds to the data with different motif signal strength obtained by fixing t = 3000, n = 200 and (l, d) = (15,5) and taking q / g as 0.2, 0.5 and 0.8.…”

Section: A Results On Simulated Datamentioning

confidence: 99%

“…Approximate qPMS algorithms aim to identify the optimum or near optimum motif. They usually adopt an optimization method, such as expectation maximization [19], Gibbs sampling [20] and genetic algorithm [21], [22], to refine a group of initial motifs. In these algorithms, MEME-ChIP [19], which is based on expectation maximization, emerges as one of the most famous motif discovery algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

Zhang

2019

IEEE Access

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Quorum planted (l, d) motif search (qPMS) is a challenging computational problem in bioinformatics, mainly for the identification of regulatory elements such as transcription factor binding sites in DNA sequences. Large DNA datasets play an important role in identifying high-quality (l, d) motifs, while most existing qPMS algorithms are too time-consuming to complete the calculation of qPMS in a reasonable time. We propose an approximate qPMS algorithm called APMS to deal with large DNA datasets mainly by accelerating neighboring substring search and filtering redundant substrings. Experimental results on them show that APMS can not only identify the implanted (l, d) motifs, but also run orders of magnitude faster than the state-of-the-art qPMS algorithms. The source code of APMS and the python wrapper for the code are freely available at https://github.com/qyu071/apms. INDEX TERMS Quorum planted (l, d) motif search, large DNA datasets, transcription factor binding sites.

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Section: A Results On Simulated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

Zhang

2019

IEEE Access

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“…However, some other researchers aimed at improving those algorithms. The satisfactory results produced by these algorithms for different optimization problems proved the importance and necessity of them [21][22][23][24][25][26]. Consequently, researchers continue to propose new algorithms in the field.…”

Section: Related Workmentioning

confidence: 99%

A new evolutionary algorithm: Learner performance based behavior algorithm

Rahman

Rashid

2021

Egyptian Informatics Journal

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A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner's studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically.

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“…The feature of nucleotide compositions (7) The feature of dinucleotide compositions (16) The feature of codon usages (64) The feature of nucleotide 4-mer compositions (256) To find conserved sequence patterns related to gene regulations [59], we check the existence of 2940, 44100 and 661500 short linear nucleotide motifs (SLim_DNAs) consisting of three to five consecutive nucleobases in the group of ISGs and non-ISGs. By using a positive 5% difference in the occurrence frequency as cut-off threshold, we find 7884 SLim_DNAs with a maximum difference in representation around 15%.…”

Section: Differences In the Coding Region Of The Canonical Transcriptsmentioning

confidence: 99%

Defining the Characteristics of Type I Interferon Stimulated Genes: Insight from Expression Data and Machine Learning

Chai

Hughes

et al. 2021

Preprint

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A virus-infected cell triggers a signalling cascade resulting in the secretion of interferons (IFNs), which in turn induce the up-regulation of IFN-stimulated genes (ISGs) that play an important role in the inhibition of the viral infection and the return to cellular homeostasis. Here, we conduct detailed analyses on 7443 features relating to evolutionary conservation, nucleotide composition, gene expression, amino acid composition, and network properties to elucidate factors associated with the stimulation of genes in response to type I IFNs. Our results show that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show significant depletion of GC-content in the coding region of their canonical transcripts, which leads to under-representation in the nucleotide compositions. Differences between ISGs and non-ISGs are also reflected in the properties of their coded amino acid sequence compositions. Network analyses show that ISG products tend to be involved in key paths but are away from hubs or bottlenecks of the human protein-protein interaction (PPI) network. Our analyses also show that interferon-repressed human genes (IRGs), which are down-regulated in the presence of IFNs, can have similar properties to ISGs, thus leading to false positives in ISG predictions. Based on these analyses, we design a machine learning framework integrating the usage of support vector machine (SVM) and feature selection algorithms. The ISG prediction achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 and demonstrates the similarity between ISGs triggered by type I and III IFNs. Our machine learning model predicts a number of genes as potential ISGs that so far have shown no significant differential expression when stimulated with IFN in the cell types and tissue types compiled in the available IFN-related databases. A webserver implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/ .

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A comprehensive survey on genetic algorithms for DNA motif prediction

Cited by 27 publications

References 71 publications

A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

A new evolutionary algorithm: Learner performance based behavior algorithm

Defining the Characteristics of Type I Interferon Stimulated Genes: Insight from Expression Data and Machine Learning

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