The evolutionary computation approach to motif discovery in biological sequences

Lones, Michael A.; Tyrrell, Andy M.

doi:10.1145/1102256.1102258

Cited by 29 publications

(14 citation statements)

References 57 publications

(69 reference statements)

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“…A nucleotide sequence is a string of letter (A, T, C, G) representing the sequence of nucleotide bases (Adenine, Cytosine, Guanine and Tyrosine) present with DNA and RNA molecules. A protein sequence is string of letters (D, E, K, R, H, N, Q, S, T, I, L, V, F, W, Y, C, M, A, G, P) representing the linear sequence of amino acids from which a protein is constructed [6].…”

Section: Biological Basis Of Motifmentioning

confidence: 99%

Data Mining Techniques for Informative Motif Discovery

Hasan¹,

Uddin²

2014

IJCA

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The discovery of motifs in biological sequence is a much explored and still exploring area of research in functional genomics since they control the expression or regulation of a group of genes involved in a similar cellular function. This paper explores the use of data mining techniques by various researchers as a solution to discover motifs in biological sequence. Although data mining techniques has not been applied extensively by researchers as compared to other algorithms. But in recent years data mining techniques has caused a wide attention by the researchers to find motifs in biological sequence. This paper is an attempt towards exploring the effectiveness of data mining techniques for motif discovery.

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Section: Biological Basis Of Motifmentioning

confidence: 99%

Data Mining Techniques for Informative Motif Discovery

Hasan¹,

Uddin²

2014

IJCA

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“…Evolutionary algorithms have also been applied to the problem of motif discovery in amino acid sequences. These and other biosequence applications are reviewed in [13].…”

Section: Related Workmentioning

confidence: 99%

A co-evolutionary framework for regulatory motif discovery

Lones

Tyrrell

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences a magnitude longer than using population clustering alone, whilst results using muscle-specific data suggest the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs.

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“…EC has certain advantages for motif discovery [2]. Evolutionary algorithms (EA) carry out global search and have relatively low three-objective optimization problem: Maximize similarity, Maximize motif length and Maximize support.…”

Section: Introductionmentioning

confidence: 99%

Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

2017

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Abstract:The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.

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The evolutionary computation approach to motif discovery in biological sequences

Cited by 29 publications

References 57 publications

Data Mining Techniques for Informative Motif Discovery

Data Mining Techniques for Informative Motif Discovery

A co-evolutionary framework for regulatory motif discovery

Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

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