Combinatorial pattern discovery for scientific data

Wang, Jason Tsong-Li; Chirn, Gung-Wei; Marr, Thomas G.; Shapiro, Bruce A.; Shasha, Dennis; Zhang, Kaizhong

doi:10.1145/191839.191863

Cited by 102 publications

(54 citation statements)

References 23 publications

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“…Mannila and Toivonen [6] proposed an approach that addresses the problem of sequential mining where each element of the sequences consists of atomic item. Similarly [11] formulated the problem of mining frequent subsequences from genetic sequences where each element of the sequences consists of an atomic item. Recently, Ezeife and Lu [4] developed an efficient technique PLWAP based on the concept of WAPTree that mines frequent subsequences of items.…”

Section: Related Workmentioning

confidence: 99%

“…The vertical counting approach is used to help us achieve a space efficient and less expensive full pruning strategy. We define two operators inter-join and intra-join (figure 3 [10,11]) to tackle both sub problems of mining sequence of itemsets and items. The inter-join operator is used whenever the tail items of two joinable (k-1)-subsequences are from different events, whereas intra-join operator is used if they are from the same event.…”

Section: Frequency Countingmentioning

confidence: 99%

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SEQUEST: mining frequent subsequences using DMA-Strips

Tan¹,

Dillon²,

Hadzic³

et al. 2006

WIT Transactions on Information and Communication Technologies, Vol 37

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Sequential patterns exist in data such as DNA string databases, occurrences of recurrent illness, etc. In this study, we present an algorithm, SEQUEST, to mine frequent subsequences from sequential patterns. The challenges of mining a very large database of sequences is computationally expensive and require large memory space. SEQUEST uses a Direct Memory Access Strips (DMA-Strips) structure to efficiently generate candidate subsequences. DMA-Strips structure provides direct access to each item to be manipulated and thus is optimized for speed and space performance. In addition, the proposed technique uses a hybrid principle of frequency counting by the vertical join approach and candidate generation by structure guided method. The structure guided method is adapted from the TMG approach used for enumerating subtrees in our previous work [8]. Experiments utilizing very large databases of sequences which compare our technique with the existing technique, PLWAP [4], demonstrate the effectiveness of our proposed technique.

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Section: Related Workmentioning

confidence: 99%

Section: Frequency Countingmentioning

confidence: 99%

SEQUEST: mining frequent subsequences using DMA-Strips

Tan¹,

Dillon²,

Hadzic³

et al. 2006

WIT Transactions on Information and Communication Technologies, Vol 37

View full text Add to dashboard Cite

show abstract

“…There are algorithms for finding frequent subsequences in a set of sequences using generalized suffix trees (GST) [25]. A GST is a trie-like structure designed for compactly representing a set of strings.…”

Section: Feature Extractionmentioning

confidence: 99%

Frequent Subsequence-Based Protein Localization

Zaı̈ane

Wang

Goebel

et al. 2006

Lecture Notes in Computer Science

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Abstract. Extracellular plant proteins are involved in numerous processes including nutrient acquisition, communication with other soil organisms, protection from pathogens, and resistance to disease and toxic metals. Insofar as these proteins are strategically positioned to play a role in resistance to environmental stress, biologists are interested in proteomic tools in analyzing extracellular proteins. In this paper, we present three methods using frequent subsequences of amino acids: one based on support vector machines (SVM), one based on boosting and FSP, a new frequent subsequence pattern method. We test our methods on a plant dataset and the experimental results show that our methods perform better than the existing approaches based on amino acid composition.

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“…To find frequent subsequences, we made use of an efficient implementation of generalized suffix tree (GST) [23] with some simple modifications. Suffix trees have been extensively used in string matching and are shown to be an effective data structure for finding common subsequences that runs in linear time [7,10].…”

Section: Stagementioning

confidence: 99%

Frequent-subsequence-based prediction of outer membrane proteins

She

Chen

Wang

et al. 2003

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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A number of medically important disease-causing bacteria (collectively called Gram-negative bacteria) are noted for the extra "outer" membrane that surrounds their cell. Proteins resident in this membrane (outer membrane proteins, or OMPs) are of primary research interest for antibiotic and vaccine drug design as they are on the surface of the bacteria and so are the most accessible targets to develop new drugs against. With the development of genome sequencing technology and bioinformatics, biologists can now deduce all the proteins that are likely produced in a given bacteria and have attempted to classify where proteins are located in a bacterial cell. However such protein localization programs are currently least accurate when predicting OMPs, and so there is a current need for the development of a better OMP classifier. Data mining research suggests that the use of frequent patterns has good performance in aiding the development of accurate and efficient classification algorithms. In this paper, we present two methods to identify OMPs based on frequent subsequences and test them on all Gramnegative bacterial proteins whose localizations have been determined by biological experiments. One classifier follows an association rule approach, while the other is based on support vector machines (SVMs). We compare the proposed methods with the state-of-the-art methods in the biological domain. The results demonstrate that our methods are better both in terms of accurately identifying OMPs and providing biological insights that increase our understanding of the structures and functions of these important proteins.

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Combinatorial pattern discovery for scientific data

Abstract: Suppose you are given a set of naturaJ entities (e.g.,

Cited by 102 publications

References 23 publications

SEQUEST: mining frequent subsequences using DMA-Strips

SEQUEST: mining frequent subsequences using DMA-Strips

Frequent Subsequence-Based Protein Localization

Frequent-subsequence-based prediction of outer membrane proteins

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