FMGA: finding motifs by genetic algorithm

Liu, F.F.M.; Tsai, Jeffrey J. P.; Chen, R.M.; Chen, S.N.; Shih, S.H.

doi:10.1109/bibe.2004.1317378

Cited by 70 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Stine et al [5] employed genetic algorithm in their structured Genetic Algorithm (St-GA) to search and to discover highly conserved motifs amongst upstream sequences of co-regulated genes. Liu et al [6] also employed genetic algorithm for finding potential motifs in the regions of Transcription Start Site (TSS). Pan et al [7] developed MacosFSpan and MacosVSpan algorithms to mine maximal frequent sequences in biological data.…”

Section: Methodsmentioning

confidence: 99%

Genetic Algorithm Based Probabilistic Motif Discovery in Unaligned Biological Sequences

Hemalatha¹,

Vivekanandan²

2008

J. of Computer Science

View full text Add to dashboard Cite

Finding motif in biosequences is the most important primitive operation in computational biology. There are many computational requirements for a motif discovery algorithm such as computer memory space requirement and computational complexity. To overcome the complexity of motif discovery, we propose an alternative solution integrating genetic algorithm and Fuzzy Art machine learning approaches for eliminating multiple sequence alignment process. Problem statement: More than a hundred methods had been proposed for motif discovery in recent years, representing a large variation with respect to both algorithmic approaches as well as the underlying models of regulatory regions. The aim of this study was to develop an alternative solution for motif discovery, which benefits from both data mining and genetic algorithm, and which at the same time eliminates the cost caused by use of multiple sequence alignment. Approach: Genetic algorithm based probabilistic Motif discovery model was designed to solve the problem. The proposed algorithm was implemented using Matlab and also tested with large DNA sequence data sets and synthetic data sets. Results: Results obtained by the proposed model to find the motif in terms of speed and length are compared with the existing method. Our proposed method finds Length of 11 in 18 sec and length of 15 in 24 sec but the existing methods finds length of 11 in 34 sec. Compare to other techniques the proposed one was outperforms the popular existing method. Conclusion: In this study, we proposed a model to discover motif in large set of unaligned sequences in considerably minimum time. Length of motif was also long. The proposed algorithm will be implemented using Matlab and was tested with large DNA sequence data sets and synthetic data sets.

show abstract

Section: Methodsmentioning

confidence: 99%

Genetic Algorithm Based Probabilistic Motif Discovery in Unaligned Biological Sequences

Hemalatha¹,

Vivekanandan²

2008

J. of Computer Science

View full text Add to dashboard Cite

show abstract

“…Liu et al (2004) introduced a genetic algorithm (GA)-based technique called FMGA. In this study, mutation and crossover operators were specialized for the motif discovery problem.…”

Section: Introductionmentioning

confidence: 99%

“…A similar procedure was also applied to the crossover operation. Special gap arrangement and penalization procedures were employed for increasing the quality of the offspring (Liu et al, 2004). Another GA-based motif discovery technique called MDGA was proposed by Che et al (2005).…”

Section: Introductionmentioning

confidence: 99%

A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences

Karaboğa¹,

Aslan²

2016

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

show abstract

“…For the purpose of explore motifs we use a total fitness score function, our approaching which part of it as the sequence alignment FMGA [13] did, we also did some modification to adapt to our TS-BFO algorithm and describe it below. Therefore, we consider the fitness score of one single sequence, defined as follows:…”

Section: Objective Functionmentioning

confidence: 99%