Genetic Algorithm Based Probabilistic Motif Discovery in Unaligned Biological Sequences
نویسندگان
چکیده
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...
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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 algorith...
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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...
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MOTIVATION The goal of motif discovery is to detect novel, unknown, and important signals from biology sequences. In most models, the importance of a motif is equal to the sum of the similarity of every single position. In 2006, Song et al. introduced Aggregated Related Column Score (ARCS) measure which includes correlation information to the evaluation of motif importance. The paper showed tha...
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ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2008
ISSN: 1549-3636
DOI: 10.3844/jcssp.2008.625.630