Genetic Algorithm Based Probabilistic Motif Discovery in Multiple Unaligned Biological Sequences
نویسندگان
چکیده
Many computational approaches have been introduced for the problem of motif identification in a set of biological sequences, which are classified according to the type of motifs discovered. In this study, we propose a model to discover motif in large set of unaligned sequences in considerably minimum time using genetic algorithm based probabilokistic Motif discovery model. The proposed algorithm will be implemented using Matlab and will be tested with large DNA sequence data sets and synthetic data sets.
منابع مشابه
Genetic Algorithm Based Probabilistic Motif Discovery in Unaligned Biological Sequences
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...
متن کاملDevelopment of an Efficient Hybrid Method for Motif Discovery in DNA Sequences
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...
متن کاملA Combinatorial Approach for Motif Discovery in Unaligned DNA Sequences
Motif (conserved pattern) modelling and finding in unaligned DNA sequences is a fundamental problem in computational biology with important applications in understanding gene regulation. Biological approaches for this problem are tedious and time-consuming. Large amounts of genome sequence data and gene expression micro-array data let us solve this problem computationally. Most computer science...
متن کاملProbabilistic Analysis of a Motif Discovery Algorithm for Multiple Sequences
We study a natural probabilistic model for motif discovery that has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet Σ. A motif G = g1g2 · · · gm is a string of m characters. Each background sequence is implanted into a probabilistically ge...
متن کاملEfficient Algorithms for Model-Based Motif Discovery from Multiple Sequences
We study a natural probabilistic model for motif discovery that has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet Σ. A motif G = g1g2 . . . gm is a string of m characters. Each background sequence is implanted a randomly generated approx...
متن کامل