Alignment of trees — an alternative to tree edit

Jiang, Tao; Wang, Lusheng; Zhang, Kaizhong

doi:10.1016/0304-3975(95)80029-9

Cited by 159 publications

(186 citation statements)

References 11 publications

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“…12 Then, aligning two structures can be translated into the problem of aligning two trees [69]. If we describe each local secondary structure as a separate tree, the problem of aligning two structures is then translated into the problem of aligning a forest of trees.…”

Section: Prediction From Unaligned and Folded Sequencesmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

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Section: Prediction From Unaligned and Folded Sequencesmentioning

confidence: 99%

Computational methods in noncoding RNA research

2007

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“…Tree matching appears in various fields such as image clustering, RNA, chemical structure analysis, and so fourth. Thus, many method were defined to measure similarity between trees, for instance largest or smallest common subtree, tree edit distance, or the transferable ratio between two trees [8].…”

Section: Tree Matchingmentioning

confidence: 99%

Transfer Learning for Content-Based Recommender Systems Using Tree Matching

Biadsy¹,

Rokach

Shmilovici

2013

Availability, Reliability, and Security in Information Systems and HCI

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Abstract. In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information for the preferences exists in another domain. Training a system to use such information across domains is shown to produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step, a correlation is found between behavior patterns in the source domain and target domain. This mapping is considered a bridge between the two. Based on the correlation and content-attributes of the items, a machine learning model is trained to predict users' ratings in the target domain. When our approach is compared to the popularity approach and KNN-cross-domain on a real world dataset, the results show that our approach outperforms both methods on an average of 83%.

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“…Consequently, special distance measures for labeled trees which exploit the structure and content of trees become necessary. Jiang, Wang and Zhang [1] suggested a measure based on a structural alignment of trees. They also prove that the structural alignment problem for trees is NP-hard if the degree of the trees is not bounded.…”

Section: Structural Similaritymentioning

confidence: 99%

“…Whereas the concept of feature vectors has proven to be very successful for unstructured content data, we particularly address the internal structure of similar objects. For this purpose we discuss several similarity measures for trees as proposed in the literature [1][2][3]. These measures are well suited for hierarchical objects and have been applied to web site analysis [4], structural similarity of XML documents [5], shape recognition [6] and chemical substructure search [4], for instance.…”

Section: Introductionmentioning

confidence: 99%

Efficient Similarity Search for Hierarchical Data in Large Databases

Kailing

Kriegel

Schönauer

et al. 2004

Lecture Notes in Computer Science

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Abstract. Structured and semi-structured object representations are getting more and more important for modern database applications. Examples for such data are hierarchical structures including chemical compounds, XML data or image data. As a key feature, database systems have to support the search for similar objects where it is important to take into account both the structure and the content features of the objects. A successful approach is to use the edit distance for tree structured data. As the computation of this measure is NP-complete, constrained edit distances have been successfully applied to trees. While yielding good results, they are still computationally complex and, therefore, of limited benefit for searching in large databases. In this paper, we propose a filter and refinement architecture to overcome this problem. We present a set of new filter methods for structural and for content-based information in tree-structured data as well as ways to flexibly combine different filter criteria. The efficiency of our methods, resulting from the good selectivity of the filters is demonstrated in extensive experiments with real-world applications.

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Alignment of trees — an alternative to tree edit

Cited by 159 publications

References 11 publications

Computational methods in noncoding RNA research

Computational methods in noncoding RNA research

Transfer Learning for Content-Based Recommender Systems Using Tree Matching

Efficient Similarity Search for Hierarchical Data in Large Databases

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