MODIS is a free audio motif discovery software developed at IRISA Rennes. Motif discovery is the task of discovering and collecting occurrences of repeating patterns in the absence of prior knowledge, or training material. In the case of speech, those motifs could be word since MODIS is tolerant to motif variability. The algorithm implementation allows to process large audio streams at a reason...

This paper explores & reviews the use of genetic algorithms by various researchers as a solution to discover motifs in molecular sequences. This survey talks about the general GA based procedure for motif discovery & reviews the latest developments in DNA motif finding using Genetic algorithms. Although GA approach has not been applied extensively by researchers as compared to other computation...

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an e...

Finding motifs in DNA sequences plays an important role in deciphering transcriptional regulatory mechanisms and drug target identification. In this paper, we propose an efficient algorithm, EDAM, for finding motifs based on frequency transformation and Minimum Bounding Rectangle (MBR) techniques. It works in three phases, frequency transformation, MBR-clique searching and motif discovery. In f...

WebMOTIFS provides a web interface that facilitates the discovery and analysis of DNA-sequence motifs. Several studies have shown that the accuracy of motif discovery can be significantly improved by using multiple de novo motif discovery programs and using randomized control calculations to identify the most significant motifs or by using Bayesian approaches. WebMOTIFS makes it easy to apply t...

WebMOTIFS provides a web interface that facilitates the discovery and analysis of DNA-sequence motifs. Several studies have shown that the accuracy of motif discovery can be significantly improved by using multiple de novo motif discovery programs and using randomized control calculations to identify the most significant motifs or by using Bayesian approaches. WebMOTIFS makes it easy to apply t...

This work presents a hybrid method for motif discovery in DNA sequences. The proposed method called SPSO-Lk, borrows the concept of Chebyshev polynomials and uses the stochastic local search to improve the performance of the basic PSO algorithm as a motif finder. The Chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed b...