Fuzzy C-Means Based DNA Motif Discovery

Karabulut, Mustafa; İbrikçi, Turgay

doi:10.1007/978-3-540-87442-3_24

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In Table , the performance measure values are actually the values of the node with the highest z ‐score. The problem of motif discovery with the clustering approach is explained in Karabulut and Ibrikci () and Mahony () in greater detail.…”

Section: Resultsmentioning

confidence: 99%

A fuzzy self‐organizing map algorithm for biological pattern recognition

Karabulut¹,

İbrikçi²

2010

Expert Systems

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Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C-means (FCM) and is based on the well-known self-organizing map (SOM). In order to show the performance of the algorithm, seven different data sets are processed. The experimental results demonstrate that the proposed algorithm has the potential to outperform SOM and FCM in terms of clustering and classification accuracy abilities. Additionally, a brief comparison is made the proposed algorithm with some previously studied 'FCM-SOM' hybrid algorithms from the literature.

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Section: Resultsmentioning

confidence: 99%

A fuzzy self‐organizing map algorithm for biological pattern recognition

Karabulut¹,

İbrikçi²

2010

Expert Systems

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“…Fuzzy clustering results gave efficient result than the crisp methods for the selected data sets. Mustafa Karabulut and Turgay Ibrikci [9] proposed fuzzy c-means algorithm for motif discovery. The soft-clustering-based machine learning methods such as FCM were useful to find the patterns in biological sequences.…”

Section: Review Of Literaturementioning

confidence: 99%

A New Hybrid Hard-Fuzzy (K-Mfcm) Data Clustering Method for Finding Cluster Centroid

Jafar¹

2018

ijarcs

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Data mining is a collection of methods used to extract useful information from large data bases. Cluster Analysis refers to the grouping of a set of data points into clusters. Most widely used partitioning methods are K-means and Fuzzy c-means (FCM) algorithms. However, they suffer from the difficulties such as random selection of initial centre values and handling outlier data points. Most of the existing clustering methods use the Euclidean distance metric. The modified fuzzy c-means algorithm (MFCM) is efficient in handling outlier data points. In this paper, a new hybrid algorithm is proposed to solve the limitations of the traditional clustering methods. The hybrid K-MFCM algorithm is tested on four real world bench mark data sets from UCI machine learning repository with various distance metrics including Euclidean, City Block and Chessboard. The cluster centroid values of hybrid algorithm are calculated for various data sets. The experimental results show that the hybrid algorithm gives good results in terms of objective function value and better fuzzy cluster validity results for chessboard distance metric than other distance metrics.

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“…The authors claimed that the tool was capable of post-processing large collections of DNA sequence motifs and of providing a non-redundant set of motifs, which could be further associated to known regulatory elements. Also, the well-known Fuzzy C-means (FCM) algorithm was applied in [20] to identify motifs in some particular regions of DNA sequences. The authors also tested K-means and Expectation-Maximization algorithms, showing that the fuzzy solution outperformed all others.…”

Section: Artificial Intelligence-based Techniquesmentioning

confidence: 99%

On the use of algorithms to discover motifs in DNA sequences

Rubio-Escudero

Martínez‐Álvarez

Martínez-Ballesteros

et al. 2011

2011 11th International Conference on Intelligent Systems Design and Applications

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insofar as their discovery may lead to major understanding of evolutive processes in living organisms.Since Stormo [37] reviewed strategies to find motifs with computer algorithms, a large amount of algorithms have been developed. A major classification of these algorithms is done according to the type of DNA data sequence used to find motifs. Although there is no universal consensus on how to divide algorithms based on the input data, three major groups are generally considered [7]:1) The algorithms that use information from coregulated genes from a single genome.2) The algorithms that use information of a single gene in multiple species.3) The algorithms that use information from phylogenetic footprints. Despite the large amount of works found and grouped in classes 2) (see [31], [38], [30]) and 3) (see [1], [12], [46]), this paper is focused on providing a general overview on current algorithms that make use of the information that promoter sequences of coregulated genes generate. Actually, these methods can also be subdivided into multiple strategies, but this work only examines those based on dictionaries, ensembles and artificial intelligence-based techniques, as they represent the classical and the leading ones, respectively.The rest of the paper is structured as follows. Section II discusses the most relevant works recently published related to dictionary-based algorithms. On the other hand, Section III presents ensemble algorithms used to find motifs in DNA. As for Section IV, it presents the latest AI works in the DNA motifs discovery field. Finally, Section V provides a brief summary of strengths and weaknesses of the reviewed strategies.II. DICTIONARY-BASED ALGORITHMS These are enumerative algorithms which, in contrast to heuristic methods, exhaustively cover the space of all possible motifs for a specific motif model description. The methodology progressively considers over-represented words, from short to long. The over-representativeness of a long word is computed as the weighted average of the short words in the current dictionary which could be part of the long word. Although this methodology is, in essence, 1074Abstract-Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory mechanisms, especially when analyzing binding sites in DNA. These sites or specific nucleotide sequences are known to be responsible for transcription processes. Thus, this work aims at providing an updated overview on strategies developed to discover meaningful motifs in DNA-related sequences, and, in particular, their attempts to find out relevant binding sites. From all existing approaches, this work is focused on dictionary, ensemble, and artificial intelligence-based algorithms since they represent the classical and the leading ones, respectively.

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Fuzzy C-Means Based DNA Motif Discovery

Cited by 5 publications

References 6 publications

A fuzzy self‐organizing map algorithm for biological pattern recognition

A fuzzy self‐organizing map algorithm for biological pattern recognition

A New Hybrid Hard-Fuzzy (K-Mfcm) Data Clustering Method for Finding Cluster Centroid

On the use of algorithms to discover motifs in DNA sequences

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