Mining Loosely Structured Motifs from Biological Data

Fassetti, Fabio; Greco, Gianluigi; Terracina, Giorgio

doi:10.1109/tkde.2008.65

Cited by 17 publications

(10 citation statements)

References 64 publications

(99 reference statements)

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“…The discovery of information encoded in biological sequences is assuming a distinguished role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually encoded in patterns frequently occurring in the sequences [16]. Remove repeating Motif discovery is the critical step to understand the regulatory mechanism of genes.…”

Section: Benefits Of Motifs Discoverymentioning

confidence: 99%

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Motif Discovery and Data Mining in Bioinformatics

Qader

Khafaji

2014

IJCT

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Bioinformatics analyses huge amounts of biological data that demands in-depth understanding. On the other hand, data mining research develops methods for discovering motifs in biosequences. Motif discovery involves benefits and challenges. We show bridge of the two fields, data mining and Bioinformatics, for successful mining of biological data. We found the motivation and justification factors lead to preferring naturalistic method research for Bioinformatics, because naturalistic method depends on real data. The method empowers Bioinformatics techniques to handle the true properties and reducing assumptions for un-modeled or uncover biodata phenomena. The empowerment comes from recognizing and understanding biodata properties and processes.

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Section: Benefits Of Motifs Discoverymentioning

confidence: 99%

“…Automatizing motif discovery has become a very active research area [16]. The aim is the extraction of all kinds of biological "meaning" of these sequences.…”

Section: Motifs Discovery Algorithmsmentioning

confidence: 99%

Motif Discovery and Data Mining in Bioinformatics

Qader

Khafaji

2014

IJCT

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“…Applications deal with financial, biological, medical, meteorological, or technical time series or sequences, for instance [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

Fisch

Gruber

Sick

2011

IEEE Trans. Knowl. Data Eng.

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In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

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“…In its basic form, a model template is just the specification of a length l for sequences of DNA basis (called boxes); thus, a motif conforming to such a template is precisely a sequence of l basis that is frequently repeated over the genome at hand. More complex model templates, instead, are supported in a few state-of-the-art systems-see, e.g., the comparative analysis by [1]-in order to look for motifs (i) that are made of several boxes at a given distance over the gene (called gap) from one another [2,6,3], and (ii) that may be partially conserved in the repetitions, since mismatches are allowed.…”

Section: Introductionmentioning

confidence: 99%

L-SME: A System for Mining Loosely Structured Motifs

Fassetti¹,

Greco²,

Terracina³

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. We present L-SME, a system to efficiently identify loosely structured motifs in genome-wide applications. L-SME is innovative in three aspects. Firstly, it handles wider classes of motifs than earlier motif discovery systems, by supporting boxes swaps and skips in the motifs structure as well as various kinds of similarity functions. Secondly, in addition to the standard exact search, it supports search via randomization in which guarantees on the quality of the results can be given a-priori based on user-definable resource (time and space) constraints. Finally, L-SME comes equipped with an intuitive graphical interface through which the structure for the motifs of interest can be defined, the discovery method can be selected, and results can be visualized. The tool is flexible and scalable, by allowing genome-wide searches for very complex motifs and is freely accessible at

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Mining Loosely Structured Motifs from Biological Data

Cited by 17 publications

References 64 publications

Motif Discovery and Data Mining in Bioinformatics

Motif Discovery and Data Mining in Bioinformatics

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

L-SME: A System for Mining Loosely Structured Motifs

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